SCONE Working Group                                             D. Druta
Internet-Draft                                              E. Halepovic
Intended status: Informational                            T. Karagioules
Expires: 3 August 2025                                              AT&T
                                                         30 January 2025


               Video Session Data Rate for SCONE protocol
              draft-druta-scone-video-session-data-rate-00

Abstract

   The SCONE protocol requires a semantically consistent way for CSPs to
   convey a throughput advice.  The Video Session Data Rate (VSDR)
   describes the formula to be applied both for setting the limit on the
   CAP side as well as for the CSP to validate conformance with the
   policy.

Status of This Memo

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   This Internet-Draft will expire on 3 August 2025.

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Table of Contents

   1.  Motivation and Drivers  . . . . . . . . . . . . . . . . . . .   2
   2.  Objectives  . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Definitions . . . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Details . . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   5.  Benefits and Uses . . . . . . . . . . . . . . . . . . . . . .   4
   6.  Assumptions . . . . . . . . . . . . . . . . . . . . . . . . .   5
   7.  Limitations . . . . . . . . . . . . . . . . . . . . . . . . .   6
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .   6
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Motivation and Drivers

   The SCONE working group has started to work on a protocol allowing
   CSPs (Communication Service Providers) to pass throughput advice to
   CAPs (Content and Application Providers) for the purpose of enforcing
   network policies associated with video throughput limits in a self-
   regulatory fashion.

   While the actual SCONE mechanism has not been defined yet, a simple,
   standard semantic representation of the throughput advice is
   necessary in order to have a common baseline measure to track overall
   video session data consumption.  This draft addresses the formula of
   metric to be used for the SCONE protocol advise.

   Since a significant percentage of the internet traffic consists of
   video and specifically Adaptive Bitrate Video, the focus is on video
   sessions and their impact on the subscription limits.  For most, the
   active stream being displayed on the screen is the only one
   delivered.  That being said, many video services use QUIC HTTP/3 as a
   delivery mechanism and take advantage of the multiplexing
   capabilities provided by the protocol.  Therefore, at any given point
   in time it is possible to have multiple video streams delivered over
   the same connection.

   In other words, the requirement is to establish a measurement that
   represents the Aggregate Bitrate of all the active video flows
   associated with a particular 4 tuple.

   An additional requirement is to establish an easily computable metric
   by both CSPs and CAPs that can be compared and measured by both
   sides.








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2.  Objectives

   On the CSP side, since most of the internet traffic is end-to-end
   encrypted, the main objective is to be able to characterize video
   sessions from network data in the absence of application-layer
   metrics.

   In addition to that, there is a need to estimate customer experience
   and network support for video delivery while assessing how is video
   service using available bandwidth.

   On the CAP side, using the same formula to compute the data usage for
   video delivery allows for a proactive implementation of video
   optimization delivery techniques in compliance and alignment with the
   network policies set for the subscription.

3.  Definitions

   A video session, from the network perspective is the set of IP flows
   carrying video (and audio and metadata, as appropriate), which are
   separated from other such flows by a timeout T.

   Data is the total volume of all flows in the session per direction of
   transmission, typically downlink for streaming video, and both
   downlink and uplink for video conferencing.

   Duration is the period from the earliest data transmission and the
   latest data transmission across all flows in the video session,
   eliminating idle times at the beginning and end of the flows, if
   exclusion is possible.

4.  Details

   Figure 1 below illustrates three video sessions, comprised of
   different video streams (three active ones with some idle periods
   representing a typical video delivery scenario with a main video
   being displayed and others being queued or pre-fetched in the
   background.













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          Session A | Delta_t > T |Session B |Delta t > T  Video Session C |
        |           |             |          |        |                    |
        |<--------->|<----------->|<-------->|<------>|<---|------|------->|
Flow #1 |XXXX       |             o|OOOOOOOOO|        |    |      |  ######|
Flow #2 |     XXXXXX|XX         / |   OOO    |        |    |      |####### |
Flow #3 |      XXXX |   \      /  |          |        |####|      |        |
        |<--------->|    \    /   |          |        |    |<---->|        |
        |           |     \  /    |          |        |    |      |        |
         Duration D     Idle period                       Delta t < T
                        (excluded)

       Figure 1: Three flow traffic for Video Session Data Rate

   The Video Session Data Rate or VSDR is ratio (expressed in Kbit/s or
   Mbit/s) of Data (Cumulative data transmitted over all the video
   flows) and duration D.

5.  Benefits and Uses

   There are several benefits to defining a standard VSDR.  For once,
   expressing the metric using the above-described formula provides
   simplicity of expression and calculation.  In addition to that, it
   enables robustness against certain types of abuse.

   Since this measure is an aggregate bitrate of active video flows, it
   should be able to provide some correlation with encoding bitrate.
   Additionally, VSDR is easily computable by both CAPs and CSPs and
   therefore induces very little extra compute cycles in the process.

   VSDR is robust to some forms of miscalculation.  First, it reflects
   the true overall data rate of the session compared to averaging or
   otherwise summarizing flow throughputs.  Therefore, it is robust to
   outliers resulting from very short flows or flows with very little
   data.  Second, it is robust to some form of miscalculation.  By
   establishing a sensible timeout T, session concatenation is
   discouraged for long periods of time resulting in artificially lower
   VSDR values.

   This method reflects more accurately the video delivery behavior and
   is measurable based on IP traffic patterns observed on the wire.

   Examples of computation:









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   CSP can compute VSDR using high-level flow records that contain start
   and end times, and data volume per direction.  No packet-level data
   is required.  Therefore, a simple accumulation of flow records can
   provide for quick computation of session start and end time for
   duration, and data volume summation, while separating sessions based
   on the timeout.  This can be done in near real time as well as later.

   CAP can compute the same VSDR using more than one method.  At the CDN
   edge note, flow monitoring towards different ASNs or subnets can
   compute VSDR in the same way as the CSP.  Web server logs can be used
   as well where multiple request/response pairs will comprise each
   flow.

   VSDR can be used in the following ways:

   For the purpose of SCONE, it provides a flexible and versatile
   vehicle to convey throughput advice or a target rate: A CSP can use
   VSDR to express to the CAP the maximum rate of delivery of the data.
   Depending on CSP network or subscription policy, it can be used as
   per-session, per-minute, or even as a percentile (e.g., 95% of
   sessions or daily data should be delivered in sessions under the
   certain VSDR).  VSDR).

   Quality of delivery estimation: As a proxy for overall delivery, its
   increase or decrease can be used as a proxy for improvement or
   degradation of the delivery of content.  Slower sessions can
   represent impact of network load and congestion, and reaction of a
   video service to it.

6.  Assumptions

   In order to measure the VSDR, on the network side it is necessary to
   correctly and accurately identify video flows.  Since the majority of
   the video traffic over the internet is end to end encrypted, the
   identification of flows is commonly based on heuristics and other
   techniques.  Although CSPs can identify the video flows, applying
   traffic shaping filters in the network can negatively impact the
   Quality of Experience for the video service as there is no video
   delivery optimization possible.  By allowing the CAP to adjust and
   optimize within the VSDR limit, the expectation is that not only the
   network policies are enforced but the video streams are efficiently
   distributed to provide users with a good user experience.









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7.  Limitations

   Since the VSDR is a measure relative to the individual CAP
   connections, it is not always representative of customer experience.
   That is because at any given time, especially on mobile connections,
   congestion can be a factor.  In addition to that, individual CAP
   traffic cannot be completely isolated from the rest of the traffic on
   the same device.  A background download for a software update, for
   example could have an impact on the overall connection throughput.

   Changes in VSDR value are comparable within each video service across
   network conditions under uniform use assumption, but not comparable
   across different video services.  For example, if we assume that a
   user population behavior of one video service is uniform across
   geographic and network locations (meaning that the distribution of
   video bitrates and general usage are comparable), then differences in
   VSDR can indicate difference in performance and QoE across network
   locations, within that video service.  However, due to differences in
   user behavior, stream design and other factors, VSDR differences are
   generally not comparable between different video services.  An
   example may be the use of the more efficient codec in one video
   service vs another.

Acknowledgements

   Thanks to Abhishek Tiwari for providing input and feedback.

Authors' Addresses

   Dan Druta
   AT&T
   Email: dd5826@att.com


   Emir Halepovic
   AT&T
   Email: emir@research.att.com


   Theo Karagioules
   AT&T
   Email: theo@research.att.com









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