Original text:
projections can be used to help with e.g. power consumption with routing architectures that provide for modular scalability.  3. 3.1.

Reviewer comments:
title: Operational Considerations for Streaming Media abbrev: Media Streaming Ops docname: draft-jholland-mops-taxonomy-01 date: 2020-01-17 category: info ipr: trust200902 area: Ops workgroup: Mops keyword: Internet-Draft standalone: yes pi: [toc, sortrefs, symrefs] author: ins: J. Holland name: Jake Holland org: Akamai Technologies, Inc. street: 150 Broadway city: Cambridge, MA 02144 country: United States of America email: EMAIL informative: CVNI: target: URL title: "Cisco Visual Networking Index: Forecast and Trends, 2017–2022 White Paper" author: org: "Cisco Systems, Inc." date: 2019-02-27 DASH: title: "Information technology -- Dynamic adaptive streaming over HTTP (DASH) -- Part 1: Media presentation description and segment formats" seriesinfo: "ISO/IEC": 23009-1:2014 date: 2014-05 MSOD: title: "Media Services On Demand: Encoder Best Practices" author: org: "Akamai Technologies, Inc." target: URL date: 2019 RFC2309: RFC3168: RFC5594: RFC5762: RFC6190: RFC8033: RFC8216: I-D.han-iccrg-arvr-transport-problem: --- abstract This document provides an overview of operational networking issues that pertain to quality of experience in delivery of video and other high-bitrate media over the internet. --- middle #Introduction {#intro} 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. What's more, 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 {{CVNI}}). 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's becoming increasingly important to consider the effects of network design decisions on application-level performance, with considerations for the impact on video delivery. This document aims to provide a taxonomy of networking issues as they relate to quality of experience in internet video delivery. The focus is on capturing characteristics of video delivery that have surprised network designers or transport experts without specific video expertise, since these highlight key differences between common assumptions in existing networking documents and observations of video delivery issues in practice. Making specific recommendations for mitigating these issues 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 these existing observed problems. #Bandwidth Provisioning ##Scaling Requirements for Media Delivery {#scaling} ###Video Bitrates Video bit-rate selection depends on many variables. Different providers give different guidelines, but an equation that approximately matches the bandwidth requirement estimates from several video providers is given in {{MSOD}}: Height and width are in pixels, and frame rate in frames per second. The actual bit-rate required for a specific video will also depend on the codec used and some other characteristics of the video itself, such as the frequency of high-detail motion, which may influence the compressability of the content, but this equation provides a rough estimate. Here are a few common resolutions used for video content, with their minimum per-user bandwidth requirements according to this formula: Approximate Bit-rate for 60fps | -----+----------------+------------------------------- 3 Mbps 8 Mbps 18 Mbps 70 Mbps ###Virtual Reality Bitrates TBD: Reference and/or adapt content from expired work-in-progress {{I-D.han-iccrg-arvr-transport-problem}}. The punchline is that it starts at a bare minimum of 22 Mbps mean with a 130 Mbps peak rate, up to 3.3 Gbps mean with 38 Gbps peak for high-end technology. ##Path Requirements The bit-rate requirements in {{scaling}} are per end-user actively consuming a media feed, so in the worst case, the bit-rate 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 up to 180 Gbps would be necessary in order for all of them to get 1080p resolution at 60 fps. 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 TBD: pros, cons, tradeoffs of caching designs at different locations within the network? Peak vs. average provisioning, and effects on peering point congestion under peak load? Provisioning issues for caching systems? ##Predictable Usage Profiles TBD: insert charts showing historical relative data usage patterns with error bars by time of day in consumer networks? Cross-ref vs. video quality by time of day in practice for some case study? Not sure if there's a good way to capture a generalized insight here, but it seems worth making the point that demand projections can be used to help with e.g. power consumption with routing architectures that provide for modular scalability. ##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 quite 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 asymetric, 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 does not, because application bandwidth usage patterns have changed. 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 ({{RFC5594}}), 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 increased their upstream bandwidth utilization. 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 ({{RFC5594}}), 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 increased 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 ISP to attempt to "throttle" these transfers, 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 applications that encourage end users to upload significant amounts of content. #Adaptive Bit Rate ##Overview Adaptive Bit-Rate (ABR) is a sort of application-level congestion response strategy in which the receiving media player attempts to detect the available bandwidth of the network path by experiment or by observing the successful application-layer download speed, then chooses a video bitrate that fits within that bandwidth, typically adjusting as changes in available bandwidth occur in the network. The choice of bit-rate occurs within the context of optimizing for some metric monitored by the video player, such as highest achievable video quality, or lowest rate of expected rebuffering events. ##Segmented Delivery ABR strategies are commonly implemented by video players using HLS {{RFC8216}} or DASH {{DASH}} to perform a reliable segment delivery of video data over HTTP. Different player implementations and receiving devices use different strategies, often proprietary algorithms, to perform the bit-rate selection and available bandwidth estimation. This kind of bandwidth-detection system can experience trouble in several ways that can be affected by networking design choices. ###Idle Time Between Segments When the bit-rate selection is successfully chosen below the available capacity of the network path, the response to a segment request will complete in less absolute time than the video bit-rate speed. The resulting idle time within the connection carrying the segments has a few surprising consequences: Mobile flow-bandwidth spectrum and timing mapping. TCP Slow-start when restarting after idle requires multiple RTTs to re-establish a throughput at the network's available capacity. On high-RTT paths or with small enough segments, this can produce a falsely low application-visible measurement of the available network capacity. ###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. Active Queue Management (AQM) systems such as PIE {{RFC8033}} or variants of RED {{RFC2309}}} that induce early random loss under congestion can mitigate this by using ECN {{RFC3168}} where available. ECN provides a congestion signal and induce a similar backoff in flows that use Explicit Congestion Notification-capable transport, but by avoiding loss avoids inducing head-of-line blocking effects in TCP connections. ##Unreliable Transport In contrast to segmented delivery, several applications use UDP or unreliable SCTP to deliver RTP or raw TS-formatted video. 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 video conferencing, or for other live-action video with interactive components, such as some sporting events. Congestion avoidance strategies for this kind of deployment vary widely in practice, ranging from some streams that are entirely unresponsive to using feedback signaling to change encoder settings (as in {{RFC5762}}), or to use fewer enhancement layers (as in {{RFC6190}}), to proprietary methods for detecting quality of experience issues and cutting off video. Note to RFC Editor: Please remove this section before publication TBD: suggestion from mic at IETF 106 (Mark Nottingham): dive into the different constraints coming from different parts of the network or distribution channels. (regarding questions about how to describe the disconnect between demand vs. capacity, while keeping good archival value.) URL TBD: suggestion from mic at IETF 106 (Dave Oran + Glenn Deen responding): pre-placement for many use cases is useful--distinguish between live vs. cacheable. "People assume high-demand == live, but not always true" with popular netflix example. (Glenn): something about latency requirements for cached vs. streaming on live vs. pre-recorded content, and breaking requirements into 2 separate charts. also: "Standardized ladder" for adaptive bit rate rates suggested, declined as out of scope. URL TBD: suggestion at the mic from IETF 106 (Aaron Falk): include industry standard metrics from citations, some standard scoping metrics may be already defined. URL #IANA Considerations This document requires no actions from IANA. #Security Considerations This document introduces no new security issues. --- back
One nit suggested, go ahead and push with that fix, and thanks!

Revised text:
projections can be used to help with e.g. power consumption with routing architectures that provide for modular scalability. <ins> 2.5. 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 quite 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 asymetric, 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 does not, because application bandwidth usage patterns have changed. 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 (RFC5594), 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. 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 (RFC5594), 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 ISP to attempt to "throttle" these transfers, 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 users who are uploading significant amounts of content. </ins> 3. 3.1.