Operational Considerations for Streaming Media

Introduction This document examines networking issues as they relate to quality of experience in Internet media delivery, especially focusing on capturing characteristics of streaming video delivery that have surprised network designers or transport experts who lack specific video expertise, since streaming media highlights key differences between common assumptions in existing networking practices and observations of video delivery issues encountered when streaming media over those existing networks. This document specifically focuses on streaming applications and defines streaming as follows:

Streaming is transmission of a continuous media from a server to a client and its simultaneous consumption by the client.
Here, "continuous media" refers to media and associated streams such as video, audio, metadata, etc. In this definition, the critical term is "simultaneous", as it is not considered streaming if one downloads a video file and plays it after the download is completed, which would be called download-and-play.

This has two implications.

First, the server's transmission rate must (loosely or tightly) match to client's consumption rate in order to provide uninterrupted playback. That is, the client must not run out of data (buffer underrun) or accept more data than it can buffer before playback (buffer overrun) as any excess media that cannot be buffered is simply discarded.
Second, the client's consumption rate is limited not only by bandwidth availability,but also media availability. The client cannot fetch media that is not available from a server yet.

This document contains

A short description of streaming video characteristics in , to set the stage for the rest of the document,
General guidance on bandwidth provisioning () and latency considerations () for streaming video delivery,
A description of adaptive encoding and adaptive delivery techniques in common use for streaming video, along with a description of the challenges media senders face in detecting the bitrate available between the media sender and media receiver, and collection of measurements by a third party for use in analytics (),
A description of existing transport protocols used for video streaming, and the issues encountered when using those protocols, along with a description of the QUIC transport protocol that we expect to be used for streaming media (),
A description of implications when streaming encrypted media (), and
A number of useful pointers for further reading on this rapidly changing subject ().

Making specific recommendations on operational practices aimed at mitigating the issues described in this document is out of scope, though some existing mitigations are mentioned in passing. The intent is to provide a point of reference for future solution proposals to use in describing how new technologies address or avoid existing observed problems.

Notes for Contributors and Reviewers Note to RFC Editor: Please remove this section and its subsections before publication. This section is to provide references to make it easier to review the development and discussion on the draft so far.

Venues for Contribution and Discussion This document is in the Github repository at: https://github.com/ietf-wg-mops/draft-ietf-mops-streaming-opcons Readers are welcome to open issues and send pull requests for this document. Substantial discussion of this document should take place on the MOPS working group mailing list (mops@ietf.org).

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Our Focus on Streaming Video As the internet has grown, an increasingly large share of the traffic delivered to end users has become video. Estimates put the total share of internet video traffic at 75% in 2019, expected to grow to 82% by 2022. This estimate projects the gross volume of video traffic will more than double during this time, based on a compound annual growth rate continuing at 34% (from Appendix D of ). A substantial part of this growth is due to increased use of streaming video, although the amount of video traffic in real-time communications (for example, online videoconferencing) has also grown significantly. While both streaming video and videoconferencing have real-time delivery and latency requirements, these requirements vary from one application to another. For example, videoconferencing demands an end-to-end (one-way) latency of a few hundreds of milliseconds whereas live streaming can tolerate latencies of several seconds. In many contexts, video traffic can be handled transparently as generic application-level traffic. However, as the volume of video traffic continues to grow, it is becoming increasingly important to consider the effects of network design decisions on application-level performance, with considerations for the impact on video delivery. Much of the focus of this document is on reliable media using HTTP over TCP, which is widely used because

support for HTTP is widely available in a wide range of operating systems,
HTTP is also used in a wide variety of other applications,
HTTP has been demonstrated to provide acceptable performance over the open Internet,
HTTP includes state of the art standardized security mechanisms, and
HTTP can make use of already-deployed caching infrastructure.

Unreliable media delivery using RTP and other UDP-based protocols is also discussed in , , , and , but it is difficult to give general guidance for these applications. For instance, when loss occurs, the most appropriate response may depend on the type of codec being used.

Bandwidth Provisioning

Scaling Requirements for Media Delivery

Video Bitrates Video bitrate selection depends on many variables including the resolution (height and width), frame rate, color depth, codec, encoding parameters, scene complexity and amount of motion. Generally speaking, as the resolution, frame rate, color depth, scene complexity and amount of motion increase, the encoding bitrate increases. As newer codecs with better compression tools are used, the encoding bitrate decreases. Similarly, a multi-pass encoding generally produces better quality output compared to single-pass encoding at the same bitrate, or delivers the same quality at a lower bitrate. Here are a few common resolutions used for video content, with typical ranges of bitrates for the two most popular video codecs .

Name	Width x Height	H.264	H.265
DVD	720 x 480	1.0 Mbps	0.5 Mbps
720p (1K)	1280 x 720	3-4.5 Mbps	2-4 Mbps
1080p (2K)	1920 x 1080	6-8 Mbps	4.5-7 Mbps
2160p (4k)	3840 x 2160	N/A	10-20 Mbps

Virtual Reality Bitrates The bitrates given in describe video streams that provide the user with a single, fixed, point of view - so, the user has no "degrees of freedom", and the user sees all of the video image that is available. Even basic virtual reality (360-degree) videos that allow users to look around freely (referred to as "three degrees of freedom", or 3DoF) require substantially larger bitrates when they are captured and encoded as such videos require multiple fields of view of the scene. Yet, due to smart delivery methods such as viewport-based or tiled-based streaming, we do not need to send the whole scene to the user. Instead, the user needs only the portion corresponding to its viewpoint at any given time (). In more immersive applications, where limited user movement ("three degrees of freedom plus", or 3DoF+) or full user movement ("six degrees of freedom", or 6DoF) is allowed, the required bitrate grows even further. In this case, immersive content is typically referred to as volumetric media. One way to represent the volumetric media is to use point clouds, where streaming a single object may easily require a bitrate of 30 Mbps or higher. Refer to and for more details.

Path Bandwidth Constraints Even when the bandwidth requirements for video streams along a path are well understood, additional analysis is required to understand the contraints on bandwidth at various points in the network. This analysis is necessary because media servers may react to bandwith constraints using two independent feedback loops:

Media servers often respond to application-level feedback from the media player that indicates a bottleneck link somewhere along the path, by adjusting the amount of media that the media server will send to the media player in a given timeframe. This is described in greater detail in .
Media servers also typically implement transport protocols with capacity-seeking congestion controllers that probe for bandwidth, and adjust the sending rate based on transport mechanisms. This is described in greater detail in .

The result is that these two (potentially competing) "helpful" mechanisms each respond to the same bottleneck with no coordination between themselves, so that each is unaware of actions taken by the other, and this can result in a quality of experience for users that is significantly lower than what could have been achieved. In one example, if a media server overestimates the available bandwidth to the media player,

the transport protocol detects loss due to congestion, and reduces its sending window size per round trip,
the media server adapts to application-level feedback from the media player, and reduces its own sending rate,
the transport protocol sends media at the new, lower rate, and confirms that this new, lower rate is "safe", because no transport-level loss is occuring, but
because the media server continues to send at the new, lower rate, the transport protocol's maximum sending rate is now limited by the amount of information the media server queues for transmission, so
the transport protocol can't probe for available path bandwidth by sending at a higher rate.

In order to avoid these types of situations, which can potentially affect all the users whose streaming media traverses a bottleneck link, there are several possible mitigations that streaming operators can use, but the first step toward mitigating a problem is knowing when that problem occurs.

Know Your Network Traffic There are many reasons why path characteristics might change suddenly, for example,

"cross traffic" that traverses part of the path, especially if this traffic is "inelastic", and does not, itself, respond to indications of path congestion.
routing changes, which can happen in normal operation, especially if the new path now includes path segments that are more heavily loaded, offer lower total bandwidth, or simply cover more distance.

Recognizing that a path carrying streaming media is "not behaving the way it normally does" is fundamental. Analytics that aid in that recognition can be more or less sophisticated, and can be as simple as noticing that the apparent round trip times for media traffic carried over TCP transport on some paths are suddenly and significantly longer than usual. Passive monitors can detect changes in the elapsed time between the acknowledgements for specific TCP segments from a TCP receiver, since TCP octet sequence numbers and acknowledgements for those sequence numbers are "carried in the clear", even if the TCP payload itself is encrypted. See for more information. As transport protocols evolve to encrypt their transport header fields, one side effect of increasing encryption is that the kind of passive monitoring, or even "performance enhancement" () that was possible with the older transport protocols (UDP, described in and TCP, described in ) is no longer possible with newer transport protocols such as QUIC (described in ). The IETF has specified a "latency spin bit" mechanism in Section 17.4 of to allow passive latency monitoring from observation points on the network path throughout the duration of a connection, but currently chartered work in the IETF is focusing on end-point monitoring and reporting, rather than on passive monitoring. One example is the "qlog" mechanism , a protocol-agnostic mechanism used to provide better visibility for encrypted protocols such as QUIC () and for HTTP/3 ().

Path Requirements The bitrate requirements in are per end-user actively consuming a media feed, so in the worst case, the bitrate demands can be multiplied by the number of simultaneous users to find the bandwidth requirements for a router on the delivery path with that number of users downstream. For example, at a node with 10,000 downstream users simultaneously consuming video streams, approximately 80 Gbps might be necessary in order for all of them to get typical content at 1080p resolution. However, when there is some overlap in the feeds being consumed by end users, it is sometimes possible to reduce the bandwidth provisioning requirements for the network by performing some kind of replication within the network. This can be achieved via object caching with delivery of replicated objects over individual connections, and/or by packet-level replication using multicast. To the extent that replication of popular content can be performed, bandwidth requirements at peering or ingest points can be reduced to as low as a per-feed requirement instead of a per-user requirement.

Caching Systems When demand for content is relatively predictable, and especially when that content is relatively static, caching content close to requesters, and pre-loading caches to respond quickly to initial requests is often useful (for example, HTTP/1.1 caching is described in ). This is subject to the usual considerations for caching - for example, how much data must be cached to make a significant difference to the requester, and how the benefits of caching and pre-loading caches balances against the costs of tracking "stale" content in caches and refreshing that content. It is worth noting that not all high-demand content is "live" content. One relevant example is when popular streaming content can be staged close to a significant number of requesters, as can happen when a new episode of a popular show is released. This content may be largely stable, so low-cost to maintain in multiple places throughout the Internet. This can reduce demands for high end-to-end bandwidth without having to use mechanisms like multicast. Caching and pre-loading can also reduce exposure to peering point congestion, since less traffic crosses the peering point exchanges if the caches are placed in peer networks, especially when the content can be pre-loaded during off-peak hours, and especially if the transfer can make use of "Lower-Effort Per-Hop Behavior (LE PHB) for Differentiated Services" , "Low Extra Delay Background Transport (LEDBAT)" , or similar mechanisms. All of this depends, of course, on the ability of a content provider to predict usage and provision bandwidth, caching, and other mechanisms to meet the needs of users. In some cases (), this is relatively routine, but in other cases, it is more difficult (, ). And as with other parts of the ecosystem, new technology brings new challenges. For example, with the emergence of ultra-low-latency streaming, responses have to start streaming to the end user while still being transmitted to the cache, and while the cache does not yet know the size of the object. Some of the popular caching systems were designed around cache footprint and had deeply ingrained assumptions about knowing the size of objects that are being stored, so the change in design requirements in long-established systems caused some errors in production. Incidents occurred where a transmission error in the connection from the upstream source to the cache could result in the cache holding a truncated segment and transmitting it to the end user's device. In this case, players rendering the stream often had the video freeze until the player was reset. In some cases the truncated object was even cached that way and served later to other players as well, causing continued stalls at the same spot in the video for all players playing the segment delivered from that cache node.

Predictable Usage Profiles Historical data shows that users consume more videos and at a higher bit rate than they did in the past on their connected devices. Improvements in the codecs that help with reducing the encoding bitrates with better compression algorithms could not have offset the increase in the demand for the higher quality video (higher resolution, higher frame rate, better color gamut, better dynamic range, etc.). In particular, mobile data usage has shown a large jump over the years due to increased consumption of entertainment as well as conversational video.

Unpredictable Usage Profiles Although TCP/IP has been used with a number of widely used applications that have symmetric bandwidth requirements (similar bandwidth requirements in each direction between endpoints), many widely-used Internet applications operate in client-server roles, with asymmetric bandwidth requirements. A common example might be an HTTP GET operation, where a client sends a relatively small HTTP GET request for a resource to an HTTP server, and often receives a significantly larger response carrying the requested resource. When HTTP is commonly used to stream movie-length video, the ratio between response size and request size can become arbitrarily large. For this reason, operators may pay more attention to downstream bandwidth utilization when planning and managing capacity. In addition, operators have been able to deploy access networks for end users using underlying technologies that are inherently asymmetric, favoring downstream bandwidth (e.g. ADSL, cellular technologies, most IEEE 802.11 variants), assuming that users will need less upstream bandwidth than downstream bandwidth. This strategy usually works, except when it fails because application bandwidth usage patterns have changed in ways that were not predicted. One example of this type of change was when peer-to-peer file sharing applications gained popularity in the early 2000s. To take one well-documented case (), the Bittorrent application created "swarms" of hosts, uploading and downloading files to each other, rather than communicating with a server. Bittorrent favored peers who uploaded as much as they downloaded, so that new Bittorrent users had an incentive to significantly increase their upstream bandwidth utilization. The combination of the large volume of "torrents" and the peer-to-peer characteristic of swarm transfers meant that end user hosts were suddenly uploading higher volumes of traffic to more destinations than was the case before Bittorrent. This caused at least one large internet service provider (ISP) to attempt to "throttle" these transfers in order to to mitigate the load that these hosts placed on their network. These efforts were met by increased use of encryption in Bittorrent, similar to an arms race, and set off discussions about "Net Neutrality" and calls for regulatory action. Especially as end users increase use of video-based social networking applications, it will be helpful for access network providers to watch for increasing numbers of end users uploading significant amounts of content.

Extremely Unpredictable Usage Profiles The causes of unpredictable usage described in were more or less the result of human choices, but we were reminded during a post-IETF 107 meeting that humans are not always in control, and forces of nature can cause enormous fluctuations in traffic patterns. In his talk, Sanjay Mishra reported that after the CoViD-19 pandemic broke out in early 2020,

Comcast's streaming and web video consumption rose by 38%, with their reported peak traffic up 32% overall between March 1 to March 30,
AT&T reported a 28% jump in core network traffic (single day in April, as compared to pre stay-at-home daily average traffic), with video accounting for nearly half of all mobile network traffic, while social networking and web browsing remained the highest percentage (almost a quarter each) of overall mobility traffic, and
Verizon reported similar trends with video traffic up 36% over an average day (pre COVID-19)}.

We note that other operators saw similar spikes during this time period. Craig Labowitz reported

Weekday peak traffic increases over 45%-50% from pre-lockdown levels,
A 30% increase in upstream traffic over their pre-pandemic levels, and
A steady increase in the overall volume of DDoS traffic, with amounts exceeding the pre-pandemic levels by 40%. (He attributed this increase to the significant rise in gaming-related DDoS attacks (), as gaming usage also increased.)

Subsequently, the Internet Architecture Board (IAB) held a COVID-19 Network Impacts Workshop in November 2020. Given a larger number of reports and more time to reflect, the following observations from the draft workshop report are worth considering.

Participants describing different types of networks reported different kinds of impacts, but all types of networks saw impacts.
Mobile networks saw traffic reductions and residential networks saw significant increases.
Reported traffic increases from ISPs and internet exchange points (IXP) over just a few weeks were as big as the traffic growth over the course of a typical year, representing a 15-20% surge in growth to land at a new normal that was much higher than anticipated.
At DE-CIX Frankfurt, the world's largest Internet Exchange Point in terms of data throughput, the year 2020 has seen the largest increase in peak traffic within a single year since the IXP was founded in 1995.
The usage pattern changed significantly as work-from-home and videoconferencing usage peaked during normal work hours, which would have typically been off-peak hours with adults at work and children at school. One might expect that the peak would have had more impact on networks if it had happened during typical evening peak hours for video streaming applications.
The increase in daytime bandwidth consumption reflected both significant increases in "essential" applications such as videoconferencing and virtual private networks (VPN), and entertainment applications as people watched videos or played games.
At the IXP-level, it was observed that port utilization increased. This phenomenon is mostly explained by a higher traffic demand from residential users.

Latency Considerations Streaming media latency refers to the "glass-to-glass" time duration, which is the delay between the real-life occurrence of an event and the streamed media being appropriately displayed on an end user's device. Note that this is different from the network latency (defined as the time for a packet to cross a network from one end to another end) because it includes video encoding/decoding and buffering time, and for most cases also ingest to an intermediate service such as a CDN or other video distribution service, rather than a direct connection to an end user. Streaming media can be usefully categorized according to the application's latency requirements into a few rough categories:

ultra low-latency (less than 1 second)
low-latency live (less than 10 seconds)
non-low-latency live (10 seconds to a few minutes)
on-demand (hours or more)

Ultra Low-Latency Ultra low-latency delivery of media is defined here as having a glass-to-glass delay target under one second. Some media content providers aim to achieve this level of latency for live media events. This introduces new challenges relative to less-restricted levels of latency requirements because this latency is the same scale as commonly observed end-to-end network latency variation (for example, due to effects such as bufferbloat (), Wi-Fi error correction, or packet reordering). These effects can make it difficult to achieve this level of latency for the general case, and may require tradeoffs in relatively frequent user-visible media artifacts. However, for controlled environments or targeted networks that provide mitigations against such effects, this level of latency is potentially achievable with the right provisioning. Applications requiring ultra low latency for media delivery are usually tightly constrained on the available choices for media transport technologies, and sometimes may need to operate in controlled environments to reliably achieve their latency and quality goals. Most applications operating over IP networks and requiring latency this low use the Real-time Transport Protocol (RTP) or WebRTC , which uses RTP for the media transport as well as several other protocols necessary for safe operation in browsers. Worth noting is that many applications for ultra low-latency delivery do not need to scale to more than a few users at a time, which simplifies many delivery considerations relative to other use cases. Recommended reading for applications adopting an RTP-based approach also includes . For increasing the robustness of the playback by implementing adaptive playout methods, refer to and . Applications with further-specialized latency requirements are out of scope for this document.

Low-Latency Live Low-latency live delivery of media is defined here as having a glass-to-glass delay target under 10 seconds. This level of latency is targeted to have a user experience similar to traditional broadcast TV delivery. A frequently cited problem with failing to achieve this level of latency for live sporting events is the user experience failure from having crowds within earshot of one another who react audibly to an important play, or from users who learn of an event in the match via some other channel, for example social media, before it has happened on the screen showing the sporting event. Applications requiring low-latency live media delivery are generally feasible at scale with some restrictions. This typically requires the use of a premium service dedicated to the delivery of live video, and some tradeoffs may be necessary relative to what is feasible in a higher latency service. The tradeoffs may include higher costs, or delivering a lower quality video, or reduced flexibility for adaptive bitrates, or reduced flexibility for available resolutions so that fewer devices can receive an encoding tuned for their display. Low-latency live delivery is also more susceptible to user-visible disruptions due to transient network conditions than higher latency services. Implementation of a low-latency live video service can be achieved with the use of low-latency extensions of HLS (called LL-HLS) and DASH (called LL-DASH) . These extensions use the Common Media Application Format (CMAF) standard that allows the media to be packaged into and transmitted in units smaller than segments, which are called chunks in CMAF language. This way, the latency can be decoupled from the duration of the media segments. Without a CMAF-like packaging, lower latencies can only be achieved by using very short segment durations. However, shorter segments means more frequent intra-coded frames and that is detrimental to video encoding quality. CMAF allows us to still use longer segments (improving encoding quality) without penalizing latency. While a LL-HLS client retrieves each chunk with a separate HTTP GET request, a LL-DASH client uses the chunked transfer encoding feature of the HTTP which allows the LL-DASH client to fetch all the chunks belonging to a segment with a single GET request. An HTTP server can transmit the CMAF chunks to the LL-DASH client as they arrive from the encoder/packager. A detailed comparison of LL-HLS and LL-DASH is given in .

Non-Low-Latency Live Non-low-latency live delivery of media is defined here as a live stream that does not have a latency target shorter than 10 seconds. This level of latency is the historically common case for segmented video delivery using HLS and DASH . This level of latency is often considered adequate for content like news or pre-recorded content. This level of latency is also sometimes achieved as a fallback state when some part of the delivery system or the client-side players do not have the necessary support for the features necessary to support low-latency live streaming. This level of latency can typically be achieved at scale with commodity CDN services for HTTP(s) delivery, and in some cases the increased time window can allow for production of a wider range of encoding options relative to the requirements for a lower latency service without the need for increasing the hardware footprint, which can allow for wider device interoperability.

On-Demand On-Demand media streaming refers to playback of pre-recorded media based on a user's action. In some cases on-demand media is produced as a by-product of a live media production, using the same segments as the live event, but freezing the manifest after the live event has finished. In other cases, on-demand media is constructed out of pre-recorded assets with no streaming necessarily involved during the production of the on-demand content. On-demand media generally is not subject to latency concerns, but other timing-related considerations can still be as important or even more important to the user experience than the same considerations with live events. These considerations include the startup time, the stability of the media stream's playback quality, and avoidance of stalls and video artifacts during the playback under all but the most severe network conditions. In some applications, optimizations are available to on-demand video that are not always available to live events, such as pre-loading the first segment for a startup time that doesn't have to wait for a network download to begin.

Adaptive Encoding, Adaptive Delivery, and Measurement Collection

Overview A simple model of video playback can be described as a video stream consumer, a buffer, and a transport mechanism that fills the buffer. The consumption rate is fairly static and is represented by the content bitrate. The size of the buffer is also commonly a fixed size. The fill process needs to be at least fast enough to ensure that the buffer is never empty, however it also can have significant complexity when things like personalization or ad workflows are introduced. The challenges in filling the buffer in a timely way fall into two broad categories: 1. content selection and 2. content variation. Content selection comprises all of the steps needed to determine which content variation to offer the client. Content variation is the number of content options that exist at any given selection point. A common example, easily visualized, is Adaptive BitRate (ABR), described in more detail below. The mechanism used to select the bitrate is part of the content selection, and the content variation are all of the different bitrate renditions. ABR is a sort of application-level response strategy in which the streaming client attempts to detect the available bandwidth of the network path by observing the successful application-layer download speed, then chooses a bitrate for each of the video, audio, subtitles and metadata (among the limited number of available options) that fits within that bandwidth, typically adjusting as changes in available bandwidth occur in the network or changes in capabilities occur during the playback (such as available memory, CPU, display size, etc.).

Adaptive Encoding Media servers can provide media streams at various bitrates because the media has been encoded at various bitrates. This is a so-called "ladder" of bitrates, that can be offered to media players as part of the manifest that describes the media being requested by the media player, so that the media player can select among the available bitrate choices. The media server may also choose to alter which bitrates are made available to players by adding or removing bitrate options from the ladder delivered to the player in subsequent manifests built and sent to the player. This way, both the player, through its selection of bitrate to request from the manifest, and the server, through its construction of the bitrates offered in the manifest, are able to affect network utilization.

Adaptive Segmented Delivery ABR playback is commonly implemented by streaming clients using HLS or DASH to perform a reliable segmented delivery of media over HTTP. Different implementations use different strategies , often relying on proprietary algorithms (called rate adaptation or bitrate selection algorithms) to perform available bandwidth estimation/prediction and the bitrate selection. Many server-player systems will do an initial probe or a very simple throughput speed test at the start of a video playback. This is done to get a rough sense of the highest video bitrate in the ABR ladder that the network between the server and player will likely be able to provide under initial network conditions. After the initial testing, clients tend to rely upon passive network observations and will make use of player side statistics such as buffer fill rates to monitor and respond to changing network conditions. The choice of bitrate occurs within the context of optimizing for some metric monitored by the client, such as highest achievable video quality or lowest chances for a rebuffering event (playback stall).

Advertising A variety of business models exist for producers of streaming media. Some content providers derive the majority of the revenue associated with streaming media directly from consumer subscriptions or one-time purchases. Others derive the majority of their streaming media associated revenue from advertising. Many content providers derive income from a mix of these and other sources of funding. The inclusion of advertising alongside or interspersed with streaming media content is therefore common in today's media landscape. Some commonly used forms of advertising can introduce potential user experience issues for a media stream. This section provides a very brief overview of a complex and evolving space, but a complete coverage of the potential issues is out of scope for this document. The same techniques used to allow a media player to switch between renditions of different bitrates at segment or chunk boundaries can also be used to enable the dynamic insertion of advertisements (herafter referred to as "ads"). Ads may be inserted either with Client Side Ad Insertion (CSAI) or Server Side Ad Insertion (SSAI). In CSAI, the ABR manifest will generally include links to an external ad server for some segments of the media stream, while in SSAI the server will remain the same during advertisements, but will include media segments that contain the advertising. In SSAI, the media segments may or may not be sourced from an external ad server like with CSAI. In general, the more targeted the ad request is, the more requests the ad service needs to be able to handle concurrently. If connectivity is poor to the ad service, this can cause rebuffering even if the underlying video assets (both content and ads) are able to be accessed quickly. The less targeted, the more likely the ad requests can be consolidated and can leverage the same caching techniques as the video content. In some cases, especially with SSAI, advertising space in a stream is reserved for a specific advertiser and can be integrated with the video so that the segments share the same encoding properties such as bitrate, dynamic range, and resolution. However, in many cases ad servers integrate with a Supply Side Platform (SSP) that offers advertising space in real-time auctions via an Ad Exchange, with bids for the advertising space coming from Demand Side Platforms (DSPs) that collect money from advertisers for delivering the advertisements. Most such Ad Exchanges use application-level protocol specifications published by the Interactive Advertising Bureau , an industry trade organization. This ecosystem balances several competing objectives, and integrating with it naively can produce surprising user experience results. For example, ad server provisioning and/or the bitrate of the ad segments might be different from that of the main video, either of which can sometimes result in video stalls. For another example, since the inserted ads are often produced independently they might have a different base volume level than the main video, which can make for a jarring user experience. Additionally, this market historically has had incidents of ad fraud (misreporting of ad delivery to end users for financial gain). As a mitigation for concerns driven by those incidents, some SSPs have required the use of players with features like reporting of ad delivery, or providing information that can be used for user tracking. Some of these and other measures have raised privacy concerns for end users. In general this is a rapidly developing space with many considerations, and media streaming operators engaged in advertising may need to research these and other concerns to find solutions that meet their user experience, user privacy, and financial goals. For further reading on mitigations, has published some standards and best practices based on user experience research.

Bitrate Detection Challenges This kind of bandwidth-measurement system can experience trouble in several ways that are affected by networking issues. Because adaptive application-level response strategies are often using rates as observed by the application layer, there are sometimes inscrutable transport-level protocol behaviors that can produce surprising measurement values when the application-level feedback loop is interacting with a transport-level feedback loop. A few specific examples of surprising phenomena that affect bitrate detection measurements are described in the following subsections. As these examples will demonstrate, it is common to encounter cases that can deliver application level measurements that are too low, too high, and (possibly) correct but varying more quickly than a lab-tested selection algorithm might expect. These effects and others that cause transport behavior to diverge from lab modeling can sometimes have a significant impact on bitrate selection and on user quality of experience, especially where players use naive measurement strategies and selection algorithms that don't account for the likelihood of bandwidth measurements that diverge from the true path capacity.

Idle Time between Segments When the bitrate selection is chosen substantially below the available capacity of the network path, the response to a segment request will typically complete in much less absolute time than the duration of the requested segment, leaving significant idle time between segment downloads. This can have a few surprising consequences:

TCP slow-start when restarting after idle requires multiple RTTs to re-establish a throughput at the network's available capacity. When the active transmission time for segments is substantially shorter than the time between segments, leaving an idle gap between segments that triggers a restart of TCP slow-start, the estimate of the successful download speed coming from the application-visible receive rate on the socket can thus end up much lower than the actual available network capacity. This in turn can prevent a shift to the most appropriate bitrate. provides some mitigations for this effect at the TCP transport layer, for senders who anticipate a high incidence of this problem.
Mobile flow-bandwidth spectrum and timing mapping can be impacted by idle time in some networks. The carrier capacity assigned to a link can vary with activity. Depending on the idle time characteristics, this can result in a lower available bitrate than would be achievable with a steadier transmission in the same network.

Some receiver-side ABR algorithms such as are designed to try to avoid this effect. Another way to mitigate this effect is by the help of two simultaneous TCP connections, as explained in for Microsoft Smooth Streaming. In some cases, the system-level TCP slow-start restart can also be disabled, for example as described in .

Head-of-Line Blocking In the event of a lost packet on a TCP connection with SACK support (a common case for segmented delivery in practice), loss of a packet can provide a confusing bandwidth signal to the receiving application. Because of the sliding window in TCP, many packets may be accepted by the receiver without being available to the application until the missing packet arrives. Upon arrival of the one missing packet after retransmit, the receiver will suddenly get access to a lot of data at the same time. To a receiver measuring bytes received per unit time at the application layer, and interpreting it as an estimate of the available network bandwidth, this appears as a high jitter in the goodput measurement, presenting as a stall, followed by a sudden leap that can far exceed the actual capacity of the transport path from the server when the hole in the received data is filled by a later retransmission. It is worth noting that more modern transport protocols such as QUIC have mitigation of head-of-line blocking as a protocol design goal. See for more details.

Wide and Rapid Variation in Path Capacity As many end devices have moved to wireless connectivity for the final hop (Wi-Fi, 5G, or LTE), new problems in bandwidth detction have emerged from radio interference and signal strength effects. Each of these technologies can experience sudden changes in capacity as the end user device moves from place to place and encounters new sources of interference. Microwave ovens, for example, can cause a throughput degradation of more than a factor of 2 while active . 5G and LTE likewise can easily see rate variation by a factor of 2 or more over a span of seconds as users move around. These swings in actual transport capacity can result in user experience issues that can be exacerbated by insufficiently responsive ABR algorithms.

Measurement Collection In addition to measurements media players use to guide their segment-by-segment adaptive streaming requests, streaming media providers may also rely on measurements collected from media players to provide analytics that can be used for decisions such as whether the adaptive encoding bitrates in use are the best ones to provide to media players, or whether current media content caching is providing the best experience for viewers. To that effect, the Consumer Technology Association (CTA) who owns the Web Application Video Ecosystem (WAVE) project has published two important specifications.

CTA-2066: Streaming Quality of Experience Events, Properties and Metrics specifies a set of media player events, properties, quality of experience (QoE) metrics and associated terminology for representing streaming media quality of experience across systems, media players and analytics vendors. While all these events, properties, metrics and associated terminology is used across a number of proprietary analytics and measurement solutions, they were used in slightly (or vastly) different ways that led to interoperability issues. CTA-2066 attempts to address this issue by defining a common terminology as well as how each metric should be computed for consistent reporting.

CTA-5004: Common Media Client Data (CMCD) Many assume that the CDNs have a holistic view into the health and performance of the streaming clients. However, this is not the case. The CDNs produce millions of log lines per second across hundreds of thousands of clients and they have no concept of a "session" as a client would have, so CDNs are decoupled from the metrics the clients generate and report. A CDN cannot tell which request belongs to which playback session, the duration of any media object, the bitrate, or whether any of the clients have stalled and are rebuffering or are about to stall and will rebuffer. The consequence of this decoupling is that a CDN cannot prioritize delivery for when the client needs it most, prefetch content, or trigger alerts when the network itself may be underperforming. One approach to couple the CDN to the playback sessions is for the clients to communicate standardized media-relevant information to the CDNs while they are fetching data. was developed exactly for this purpose.

Unreliable Transport In contrast to segmented delivery, several applications use unreliable UDP or SCTP with its "partial reliability" extension to deliver Media encapsulated in RTP or raw MPEG Transport Stream ("MPEG-TS")-formatted video , when the media is being delivered in situations such as broadcast and live streaming, that better tolerate occasional packet loss without retransmission. Under congestion and loss, this approach generally experiences more video artifacts with fewer delay or head-of-line blocking effects. Often one of the key goals is to reduce latency, to better support applications like videoconferencing, or for other live-action video with interactive components, such as some sporting events. The Secure Reliable Transport protocol also uses UDP in an effort to achieve lower latency for streaming media, although it adds reliability at the application layer. Congestion avoidance strategies for deployments using unreliable transport protocols vary widely in practice, ranging from being entirely unresponsive to congestion, to using feedback signaling to change encoder settings (as in ), to using fewer enhancement layers (as in ), to using proprietary methods to detect "quality of experience" issues and turn off video in order to allow less bandwidth-intensive media such as audio to be delivered. More details about congestion avoidance strategies used with unreliable transport protocols are included in .

Evolution of Transport Protocols and Transport Protocol Behaviors Because networking resources are shared between users, a good place to start our discussion is how contention between users, and mechanisms to resolve that contention in ways that are "fair" between users, impact streaming media users. These topics are closely tied to transport protocol behaviors. As noted in , ABR response strategies such as HLS or DASH are attempting to respond to changing path characteristics, and underlying transport protocols are also attempting to respond to changing path characteristics. For most of the history of the Internet, these transport protocols, described in and , have had relatively consistent behaviors that have changed slowly, if at all, over time. Newly standardized transport protocols like QUIC can behave differently from existing transport protocols, and these behaviors may evolve over time more rapidly than currently-used transport protocols. For this reason, we have included a description of how the path characteristics that streaming media providers may see are likely to evolve over time.

UDP and Its Behavior For most of the history of the Internet, we have trusted UDP-based applications to limit their impact on other users. One of the strategies used was to use UDP for simple query-response application protocols, such as DNS, which is often used to send a single-packet request to look up the IP address for a DNS name, and return a single-packet response containing the IP address. Although it is possible to saturate a path between a DNS client and DNS server with DNS requests, in practice, that was rare enough that DNS included few mechanisms to resolve contention between DNS users and other users (whether they are also using DNS, or using other application protocols). In recent times, the usage of UDP-based applications that were not simple query-response protocols has grown substantially, and since UDP does not provide any feedback mechanism to senders to help limit impacts on other users, application-level protocols such as RTP have been responsible for the decisions that TCP-based applications have delegated to TCP - what to send, how much to send, and when to send it. So, the way some UDP-based applications interact with other users has changed. It is also worth pointing out that because UDP has no transport-layer feedback mechanisms, UDP-based applications that send and receive substantial amounts of information are expected to provide their own feedback mechanisms. This expectation is most recently codified in Best Current Practice . RTP relies on RTCP Sender and Receiver Reports as its own feedback mechanism, and even includes Circuit Breakers for Unicast RTP Sessions for situations when normal RTP congestion control has not been able to react sufficiently to RTP flows sending at rates that result in sustained packet loss. The notion of "Circuit Breakers" has also been applied to other UDP applications in , such as tunneling packets over UDP that are potentially not congestion-controlled (for example, "Encapsulating MPLS in UDP", as described in ). If streaming media is carried in tunnels encapsulated in UDP, these media streams may encounter "tripped circuit breakers", with resulting user-visible impacts.

TCP and Its Behavior For most of the history of the Internet, we have trusted TCP to limit the impact of applications that sent a significant number of packets, in either or both directions, on other users. Although early versions of TCP were not particularly good at limiting this impact , the addition of Slow Start and Congestion Avoidance, as described in , were critical in allowing TCP-based applications to "use as much bandwidth as possible, but to avoid using more bandwidth than was possible". Although dozens of RFCs have been written refining TCP decisions about what to send, how much to send, and when to send it, since 1988 the signals available for TCP senders remained unchanged - end-to-end acknowledgements for packets that were successfully sent and received, and packet timeouts for packets that were not. The success of the largely TCP-based Internet is evidence that the mechanisms TCP used to achieve equilibrium quickly, at a point where TCP senders do not interfere with other TCP senders for sustained periods of time, have been largely successful. The Internet continued to work even when the specific mechanisms used to reach equilibrium changed over time. Because TCP provides a common tool to avoid contention, as some TCP-based applications like FTP were largely replaced by other TCP-based applications like HTTP, the transport behavior remained consistent. In recent times, the TCP goal of probing for available bandwidth, and "backing off" when a network path is saturated, has been supplanted by the goal of avoiding growing queues along network paths, which prevent TCP senders from reacting quickly when a network path is saturated. Congestion control mechanisms such as COPA and BBR make these decisions based on measured path delays, assuming that if the measured path delay is increasing, the sender is injecting packets onto the network path faster than the receiver can accept them, so the sender should adjust its sending rate accordingly. Although TCP behavior has changed over time, the common practice of implementing TCP as part of an operating system kernel has acted to limit how quickly TCP behavior can change. Even with the widespread use of automated operating system update installation on many end-user systems, streaming media providers could have a reasonable expectation that they could understand TCP transport protocol behaviors, and that those behaviors would remain relatively stable in the short term.

The QUIC Protocol and Its Behavior The QUIC protocol, developed from a proprietary protocol into an IETF standards-track protocol , turns many of the statements made in and on their heads. Although QUIC provides an alternative to the TCP and UDP transport protocols, QUIC is itself encapsulated in UDP. As noted elsewhere in , the QUIC protocol encrypts almost all of its transport parameters, and all of its payload, so any intermediaries that network operators may be using to troubleshoot HTTP streaming media performance issues, perform analytics, or even intercept exchanges in current applications will not work for QUIC-based applications without making changes to their networks. describes the implications of media encryption in more detail. While QUIC is designed as a general-purpose transport protocol, and can carry different application-layer protocols, the current standardized mapping is for HTTP/3 , which describes how QUIC transport features are used for HTTP. The convention is for HTTP/3 to run over UDP port 443 but this is not a strict requirement. When HTTP/3 is encapsulated in QUIC, which is then encapsulated in UDP, streaming operators (and network operators) might see UDP traffic patterns that are similar to HTTP(S) over TCP. Since earlier versions of HTTP(S) rely on TCP, UDP ports may be blocked for any port numbers that are not commonly used, such as UDP 53 for DNS. Even when UDP ports are not blocked and HTTP/3 can flow, streaming operators (and network operators) may severely rate-limit this traffic because they do not expect to see legitimate high-bandwidth traffic such as streaming media over the UDP ports that HTTP/3 is using. As noted in , because TCP provides a reliable, in-order delivery service for applications, any packet loss for a TCP connection causes "head-of-line blocking", so that no TCP segments arriving after a packet is lost will be delivered to the receiving application until the lost packet is retransmitted, allowing in-order delivery to the application to continue. As described in , QUIC connections can carry multiple streams, and when packet losses do occur, only the streams carried in the lost packet are delayed. A QUIC extension currently being specified () adds the capability for "unreliable" delivery, similar to the service provided by UDP, but these datagrams are still subject to the QUIC connection's congestion controller, providing some transport-level congestion avoidance measures, which UDP does not. As noted in , there is an increasing interest in transport protocol behaviors that respond to delay measurements, instead of responding to packet loss. These behaviors may deliver improved user experience, but in some cases have not responded to sustained packet loss, which exhausts available buffers along the end-to-end path that may affect other users sharing that path. The QUIC protocol provides a set of congestion control hooks that can be used for algorithm agility, and defines a basic algorithm with transport behavior that is roughly similar to TCP NewReno . However, QUIC senders can and do unilaterally choose to use different algorithms such as loss-based CUBIC , delay-based COPA or BBR, or even something completely different. We do have experience with deploying new congestion controllers without melting the Internet (CUBIC is one example), but the point mentioned in about TCP being implemented in operating system kernels is also different with QUIC. Although QUIC can be implemented in operating system kernels, one of the design goals when this work was chartered was "QUIC is expected to support rapid, distributed development and testing of features", and to meet this expectation, many implementers have chosen to implement QUIC in user space, outside the operating system kernel, and to even distribute QUIC libraries with their own applications. The decision to deploy a new version of QUIC is relatively uncontrolled, compared to other widely used transport protocols, and this can include new transport behaviors that appear without much notice except to the QUIC endpoints. At IETF 105, Christian Huitema and Brian Trammell presented a talk on "Congestion Defense in Depth" , that explored potential concerns about new QUIC congestion controllers being broadly deployed without the testing and instrumentation that current major content providers routinely include. The sense of the room at IETF 105 was that the current major content providers understood what is at stake when they deploy new congestion controllers, but this presentation, and the related discussion in TSVAREA minutes from IETF 105 (, are still worth a look for new and rapidly growing content providers. It is worth considering that if TCP-based HTTP traffic and UDP-based HTTP/3 traffic are allowed to enter operator networks on roughly equal terms, questions of fairness and contention will be heavily dependent on interactions between the congestion controllers in use for TCP-based HTTP traffic and UDP-based HTTP/3 traffic. More broadly, discusses manageability of the QUIC transport protocol, focusing on the implications of QUIC's design and wire image on network operations involving QUIC traffic. It discusses what network operators can consider in some detail.

Streaming Encrypted Media "Encrypted Media" has at least three meanings:

Media encrypted at the application layer, typically using some sort of Digital Rights Management (DRM) system, and typically remaining encrypted "at rest", when senders and receivers store it.
Media encrypted by the sender at the transport layer, and remaining encrypted until it reaches the ultimate media consumer (in this document, referred to as "end-to-end media encryption").
Media encrypted by the sender at the transport layer, and remaining encrypted until it reaches some intermediary that is not the ultimate media consumer, but has credentials allowing decryption of the media content. This intermediary may examine and even transform the media content in some way, before forwarding re-encrypted media content (in this document referred to as "hop-by-hop media encryption").

Both "hop-by-hop" and "end-to-end" encrypted transport may carry media that is, in addition, encrypted at the application layer. Each of these encryption strategies is intended to achieve a different goal. For instance, application-level encryption may be used for business purposes, such as avoiding piracy or enforcing geographic restrictions on playback, while transport-layer encryption may be used to prevent media steam manipulation or to protect manifests. This document does not take a position on whether those goals are "valid" (whatever that might mean). In this document, we will focus on media encrypted at the transport layer, whether encrypted "hop-by-hop" or "end-to-end". Because media encrypted at the application layer will only be processed by application-level entities, this encryption does not have transport-layer implications. Both "End-to-End" and "Hop-by-Hop" media encryption have specific implications for streaming operators. These are described in and .

General Considerations for Media Encryption The use of strong encryption does provide confidentiality for encrypted streaming media, from the sender to either an intermediary or the ultimate media consumer, and this does prevent Deep Packet Inspection by any intermediary that does not possess credentials allowing decryption. However, even encrypted content streams may be vulnerable to traffic analysis. An intermediary that can identify an encrypted media stream without decrypting it, may be able to "fingerprint" the encrypted media stream of known content, and then match the targeted media stream against the fingerprints of known content. This protection can be lessened if a media provider is repeatedly encrypting the same content. is an example of what is possible when identifying HTTPS-protected videos over TCP transport, based either on the length of entire resources being transferred, or on characteristic packet patterns at the beginning of a resource being transferred. If traffic analysis is successful at identifying encrypted content and associating it with specific users, this breaks privacy as certainly as examining decrypted traffic. Because HTTPS has historically layered HTTP on top of TLS, which is in turn layered on top of TCP, intermediaries do have access to unencrypted TCP-level transport information, such as retransmissions, and some carriers exploited this information in attempts to improve transport-layer performance . The most recent standardized version of HTTPS, HTTP/3 , uses the QUIC protocol as its transport layer. QUIC relies on the TLS 1.3 initial handshake only for key exchange , and encrypts almost all transport parameters itself, with the exception of a few invariant header fields. In the QUIC short header, the only transport-level parameter which is sent "in the clear" is the Destination Connection ID , and even in the QUIC long header, the only transport-level parameters sent "in the clear" are the Version, Destination Connection ID, and Source Connection ID. For these reasons, HTTP/3 is significantly more "opaque" than HTTPS with HTTP/1 or HTTP/2.

Considerations for "Hop-by-Hop" Media Encryption Although the IETF has put considerable emphasis on end-to-end streaming media encryption, there are still important use cases that require the insertion of intermediaries. There are a variety of ways to involve intermediaries, and some are much more intrusive than others. From a content provider's perspective, a number of considerations are in play. The first question is likely whether the content provider intends that intermediaries are explicitly addressed from endpoints, or whether the content provider is willing to allow intermediaries to "intercept" streaming content transparently, with no awareness or permission from either endpoint. If a content provider does not actively work to avoid interception by intermediaries, the effect will be indistinguishable from "impersonation attacks", and endpoints cannot be assumed of any level of privacy. Assuming that a content provider does intend to allow intermediaries to participate in content streaming, and does intend to provide some level of privacy for endpoints, there are a number of possible tools, either already available or still being specified. These include

Server And Network assisted DASH - this specification introduces explicit messaging between DASH clients and network elements or between various network elements for the purpose of improving the efficiency of streaming sessions by providing information about real-time operational characteristics of networks, servers, proxies, caches, CDNs, as well as DASH client's performance and status.
"Double Encryption Procedures for the Secure Real-Time Transport Protocol (SRTP)" - this specification provides a cryptographic transform for the Secure Real-time Transport Protocol that provides both hop-by-hop and end-to-end security guarantees.
Secure Media Frames - is closely tied to SRTP, and this close association impeded widespread deployment, because it could not be used for the most common media content delivery mechanisms. A more recent proposal, Secure Media Frames , also provides both hop-by-hop and end-to-end security guarantees, but can be used with other transport protocols beyond SRTP.

If a content provider chooses not to involve intermediaries, this choice should be carefully considered. As an example, if media manifests are encrypted end-to-end, network providers who had been able to lower offered quality and reduce on their networks will no longer be able to do that. Some resources that might inform this consideration are in (for WebRTC) and (for HTTP/3 and QUIC).

Considerations for "End-to-End" Media Encryption "End-to-end" media encryption offers the potential of providing privacy for streaming media consumers, with the idea being that if an unauthorized intermediary can't decrypt streaming media, the intermediary can't use Deep Packet Inspection (DPI) to examine HTTP request and response headers and identify the media content being streamed. "End-to-end" media encryption has become much more widespread in the years since the IETF issued "Pervasive Monitoring Is an Attack" as a Best Current Practice, describing pervasive monitoring as a much greater threat than previously appreciated. After the Snowden disclosures, many content providers made the decision to use HTTPS protection - HTTP over TLS - for most or all content being delivered as a routine practice, rather than in exceptional cases for content that was considered "sensitive". Unfortunately, as noted in , there is no way to prevent pervasive monitoring by an "attacker", while allowing monitoring by a more benign entity who "only" wants to use DPI to examine HTTP requests and responses in order to provide a better user experience. If a modern encrypted transport protocol is used for end-to-end media encryption, intermediary streaming operators are unable to examine transport and application protocol behavior. As described in , only an intermediary streaming operator who is explicitly authorized to examine packet payloads, rather than intercepting packets and examining them without authorization, can continue these practices. said that "The IETF will strive to produce specifications that mitigate pervasive monitoring attacks", so streaming operators should expect the IETF's direction toward preventing unauthorized monitoring of IETF protocols to continue for the forseeable future.

Further Reading and References Editor's note: This section is to be kept in a living document where future references, links and/or updates to the existing references will be reflected. That living document is likely to be an IETF-owned Wiki: https://tinyurl.com/streaming-opcons-reading

Industry Terminology

SVA Glossary: https://glossary.streamingvideoalliance.org/
Datazoom Video Player Data Dictionary: https://help.datazoom.io/hc/en-us/articles/360031323311
Datazoom Video Metrics Encyclopedia: https://help.datazoom.io/hc/en-us/articles/360046177191

Surveys and Tutorials

Encoding The following papers describe how video is encoded, different video encoding standards and tradeoffs in selecting encoding parameters.

Overview of the Versatile Video Coding (VVC) Standard and its Applications (https://ieeexplore.ieee.org/document/9503377)
Video Compression - From Concepts to the H.264/AVC Standard (https://ieeexplore.ieee.org/document/1369695)
Developments in International Video Coding Standardization After AVC, With an Overview of Versatile Video Coding (VVC) (https://ieeexplore.ieee.org/document/9328514)
A Technical Overview of AV1 (https://ieeexplore.ieee.org/document/9363937)
CTU Depth Decision Algorithms for HEVC: A Survey (https://arxiv.org/abs/2104.08328)

Packaging The following papers summarize the methods for selecting packaging configurations such as the resolution-bitrate pairs, segment durations, use of constant vs. variable-duration segments, etc.

Deep Reinforced Bitrate Ladders for Adaptive Video Streaming (https://dl.acm.org/doi/10.1145/3458306.3458873)
Comparing Fixed and Variable Segment Durations for Adaptive Video Streaming: a Holistic Analysis (https://dl.acm.org/doi/10.1145/3339825.3391858)

Content Delivery The following links describe some of the issues and solutions regarding the interconnecting of the content delivery networks.

Open Caching: Open standards for Caching in ISP Networks: https://www.streamingvideoalliance.org/working-group/open-caching/
Netflix Open Connect: https://openconnect.netflix.com

ABR Algorithms The two surveys describe and compare different rate-adaptation algorithms in terms of different metrics like achieved bitrate/quality, stall rate/duration, bitrate switching frequency, fairness, network utilization, etc.

A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (https://ieeexplore.ieee.org/document/8424813)
A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming Over HTTP (https://ieeexplore.ieee.org/document/7884970)

Low-Latency Live Adaptive Streaming The following papers describe the peculiarities of adaptive streaming in low-latency live streaming scenarios.

Catching the Moment with LoL+ in Twitch-like Low-latency Live Streaming Platforms (https://ieeexplore.ieee.org/document/9429986)
Data-driven Bandwidth Prediction Models and Automated Model Selection for Low Latency (https://ieeexplore.ieee.org/document/9154522)
Performance Analysis of ACTE: A Bandwidth Prediction Method for Low-latency Chunked Streaming (https://dl.acm.org/doi/10.1145/3387921)
Online Learning for Low-latency Adaptive Streaming (https://dl.acm.org/doi/10.1145/3339825.3397042)
Tightrope Walking in Low-latency Live Streaming: Optimal Joint Adaptation of Video Rate and Playback Speed (https://dl.acm.org/doi/10.1145/3458305.3463382)
Content-aware Playback Speed Control for Low-latency Live Streaming of Sports (https://dl.acm.org/doi/10.1145/3458305.3478437)

Server/Client/Network Collaboration The following papers explain the benefits of server and network assistance in client-driven streaming systems. There is also a good reference about how congestion affects video quality and how rate control works in streaming applications.

Manus Manum Lavat: Media Clients and Servers Cooperating with Common Media Client/Server Data (https://dl.acm.org/doi/10.1145/3472305.3472886)
Common media client data (CMCD): initial findings (https://dl.acm.org/doi/10.1145/3458306.3461444)
SDNDASH: Improving QoE of HTTP Adaptive Streaming Using Software Defined Networking (https://dl.acm.org/doi/10.1145/2964284.2964332)
Caching in HTTP Adaptive Streaming: Friend or Foe? (https://dl.acm.org/doi/10.1145/2578260.2578270)
A Survey on Multi-Access Edge Computing Applied to Video Streaming: Some Research Issues and Challenges (https://ieeexplore.ieee.org/document/9374553)
The Ultimate Guide to Internet Congestion Control (https://www.compiralabs.com/ultimate-guide-congestion-control)

QoE Metrics The following papers describe various QoE metrics one can use in streaming applications.

QoE Management of Multimedia Streaming Services in Future Networks: a Tutorial and Survey (https://ieeexplore.ieee.org/document/8930519)
A Survey on Quality of Experience of HTTP Adaptive Streaming (https://ieeexplore.ieee.org/document/6913491)
QoE Modeling for HTTP Adaptive Video Streaming-A Survey and Open Challenges (https://ieeexplore.ieee.org/document/8666971)

Point Clouds and Immersive Media The following papers explain the latest developments in the immersive media domain (for video and audio) and the developing standards for such media.

A Survey on Adaptive 360o Video Streaming: Solutions, Challenges and Opportunities (https://ieeexplore.ieee.org/document/9133103)
MPEG Immersive Video Coding Standard (https://ieeexplore.ieee.org/document/9374648)
Emerging MPEG Standards for Point Cloud Compression (https://ieeexplore.ieee.org/document/8571288)
Compression of Sparse and Dense Dynamic Point Clouds--Methods and Standards (https://ieeexplore.ieee.org/document/9457097)
MPEG Standards for Compressed Representation of Immersive Audio (https://ieeexplore.ieee.org/document/9444109)
An Overview of Omnidirectional MediA Format (OMAF) (https://ieeexplore.ieee.org/document/9380215)
From Capturing to Rendering: Volumetric Media Delivery with Six Degrees of Freedom (https://ieeexplore.ieee.org/document/9247522)

Open-Source Tools

5G-MA: https://www.5g-mag.com/reference-tools
dash.js: http://reference.dashif.org/dash.js/latest/samples/
DASH-IF Conformance: https://conformance.dashif.org
ExoPlayer: https://github.com/google/ExoPlayer
FFmpeg: https://www.ffmpeg.org/
GPAC: https://gpac.wp.imt.fr/
hls.js: https://github.com/video-dev/hls.js
OBS Studio: https://obsproject.com/
Shaka Player: https://github.com/google/shaka-player
Shaka Packager: https://github.com/google/shaka-packager
Traffic Control CDN: https://trafficcontrol.apache.org/
VideoLAN: https://www.videolan.org/projects/
video.js: https://github.com/videojs/video.js

Technical Events

ACM Mile High Video (MHV): https://mile-high.video/
ACM Multimedia Systems (MMSys): https://acmmmsys.org
ACM Multimedia (MM): https://acmmm.org
ACM NOSSDAV: https://www.nossdav.org/
ACM Packet Video: https://packet.video/
Demuxed and meetups: https://demuxed.com/ and https://demuxed.com/events/
DVB World: https://www.dvbworld.org
EBU BroadThinking: https://tech.ebu.ch/events/broadthinking2021
IBC Conference: https://show.ibc.org/conference/ibc-conference
IEEE Int. Conf. on Multimedia and Expo (ICME)
Media Web Symposium: https://www.fokus.fraunhofer.de/de/go/mws
Live Video Stack: https://sh2021.livevideostack.com
Picture Coding Symp. (PCS)
SCTE Expo: https://expo.scte.org/

List of Organizations Working on Streaming Media

3GPP SA4: https://www.3gpp.org/specifications-groups/sa-plenary/sa4-codec
5G-MAG: https://www.5g-mag.com/
AOM: http://aomedia.org/
ATSC: https://www.atsc.org/
CTA WAVE: https://cta.tech/Resources/Standards/WAVE-Project
DASH Industry Forum: https://dashif.org/
DVB: https://dvb.org/
HbbTV: https://www.hbbtv.org/
HESP Alliance: https://www.hespalliance.org/
IAB: https://www.iab.com/
MPEG: https://www.mpegstandards.org/
Streaming Video Alliance: https://www.streamingvideoalliance.org/
SCTE: https://www.scte.org/
SMPTE: https://www.smpte.org/
SRT Alliance: https://www.srtalliance.org/
Video Services Forum: https://vsf.tv/
VQEG: https://www.its.bldrdoc.gov/vqeg/vqeg-home.aspx
W3C: https://www.w3.org/

Topics to Keep an Eye on

5G and Media 5G new radio and systems technologies provide new functionalities for video distribution. 5G targets not only smartphones, but also new devices such as augmented reality glasses or automotive receivers. Higher bandwidth, lower latencies, edge and cloud computing functionalities, service-based architectures, low power consumption, broadcast/multicast functionalities and other network functions come hand in hand with new media formats and processing capabilities promising better and more consistent quality for traditional video streaming services as well as enabling new experiences such as immersive media and augmented realities.

5G Multimedia Standardization (https://www.riverpublishers.com/journal_read_html_article.php?j=JICTS/6/1/8)

Ad Insertion Ads can be inserted at different stages in the streaming workflow, on the server side or client side. The DASH-IF guidelines detail server-side ad-insertion with period replacements based on manipulating the manifest. HLS interstitials provide a similar approach. The idea is that the manifest can be changed and point to a sub-playlist of segments, possibly located on a different location. This approach results in efficient resource usage in the network, as duplicate caching is avoided, but some intelligence at the player is needed to deal with content transitions (e.g., codec changes, timeline gaps, etc.). Player support for such content is gradually maturing. Other important technologies for ad insertion include signalling of ads and breaks that is still typically based on SCTE-35 for HLS and SCTE-214 for DASH. Such signals provide useful information for scheduling the ads and contacting ad servers. The usage of SCTE-35 for ad insertion is popular in the broadcast industry, while the exact usage in the OTT space is still being discussed in SCTE. Another important technology is identification of ads, such as based on ad-id or other commercial entities that provide such services. The identification of the ad in a manifest or stream is usually standardized by SMPTE. Other key technologies for ad insertion include tracking of viewer impressions, usually based on Video Ad Serving Template (VAST) defined by IAB.

DASH-IF Ad Insertion Guidelines: https://dashif.org/docs/CR-Ad-Insertion-r7.pdf
SCTE-214-1: https://www.scte.org/standards-development/library/standards-catalog/ansiscte-214-1-2016/
RP 2092-1:2015 - SMPTE Recommended Practice - Advertising Digital Identifier (Ad-ID) Representations: https://ieeexplore.ieee.org/document/7291518
IAB Tech Lab Digital Video Studio: https://iabtechlab.com/audio-video/tech-lab-digital-video-suite/

Contribution and Ingest There are different contribution and ingest specifications dealing with different use cases. A common case is contribution that previously happened over satellite to a broadcast or streaming headend. RIST and SRT are examples of such contribution protocols. Within a streaming headend the encoder and packager/CDN may have an ingest/contribution interface as well. This is specified by the DASH-IF Ingest.

DASH-IF Ingest: https://github.com/Dash-Industry-Forum/Ingest
RIST: https://www.rist.tv/
SRT: https://github.com/Haivision/srt

Synchronized Encoding and Packaging Practical streaming headends need redundant encoders and packagers to operate without glitches and blackouts. The redundant operation requires synchronization between two or more encoders and also between two or more packagers that possibly handle different inputs and outputs, generating compatible inter-changeable output representations. This problem is important for anyone developing a streaming headend at scale, and the synchronization problem is currently under discussion in the wider community. Follow the developments at: https://sites.google.com/view/encodersyncworkshop/home

WebRTC-Based Streaming WebRTC is increasingly being used for streaming of time-sensitive content such as live sporting events. Innovations in cloud computing allow implementers to efficiently scale delivery of content using WebRTC. Support for WebRTC communication is available on all modern web browsers and is available on native clients for all major platforms.

DASH-IF WebRTC Discussions: https://dashif.org/webRTC/
Overview of WebRTC: https://webrtc.org/

IANA Considerations This document requires no actions from IANA.

Security Considerations Security is an important matter for streaming media applications and it was briefly touched on in . This document itself introduces no new security issues.

Acknowledgments Thanks to Alexandre Gouaillard, Aaron Falk, Chris Lemmons, Dave Oran, Glenn Deen, Kyle Rose, Leslie Daigle, Lucas Pardue, Mark Nottingham, Matt Stock, Mike English, Renan Krishna, Roni Even, Sanjay Mishra, and Will Law for very helpful suggestions, reviews and comments.