MOPS                                                          R. Krishna
Internet-Draft                               InterDigital Europe Limited
Intended status: Informational                                 A. Rahman
Expires: May 3, 2021                    InterDigital Communications, LLC
                                                        October 30, 2020

 Media Operations Use Case for an Augmented Reality Application on Edge
                        Computing Infrastructure


   A use case describing transmission of an application on the Internet
   that has several unique characteristics of Augmented Reality (AR)
   applications is presented for the consideration of the Media
   Operations (MOPS) Working Group.  One key requirement identified is
   that the Adaptive-Bit-Rate (ABR) algorithms' current usage of
   policies based on heuristics and models is inadequate for AR
   applications running on the Edge Computing infrastructure.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on May 3, 2021.

Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   ( in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect

Krishna & Rahman           Expires May 3, 2021                  [Page 1]

Internet-Draft              MOPS AR Use Case                October 2020

   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions used in this document . . . . . . . . . . . . . .   3
   3.  Use Case  . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   3
   5.  Informative References  . . . . . . . . . . . . . . . . . . .   5
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Introduction

   The MOPS draft, [I-D.ietf-mops-streaming-opcons], provides an
   overview of operational networking issues that pertain to Quality of
   Experience (QoE) in delivery of video and other high-bitrate media
   over the Internet.  However, as it does not cover the increasingly
   large number of applications with Augmented Reality (AR)
   characteristics and their requirements on ABR algorithms, the
   discussion in this draft compliments the overview presented in that
   draft [I-D.ietf-mops-streaming-opcons].

   Future AR applications will bring several requirements for the
   Internet and the mobile devices running these applications.  AR
   applications require a real-time processing of video streams to
   recognize specific objects.  This is then used to overlay information
   on the video being displayed to the user.  In addition some AR
   applications will also require generation of new video frames to be
   played to the user.  In order to run future applications with AR
   characteristics on mobile devices, computationally intensive tasks
   need to be offloaded to resources provided by Edge Computing.

   Edge Computing is an emerging paradigm where computing resources and
   storage are made available in close network proximity at the edge of
   the Internet to mobile devices and sensors [EDGE_1], [EDGE_2].

   Adaptive-Bit-Rate (ABR) algorithms currently base their policy for
   bit-rate selection on heuristics or models of the deployment
   environment that do not account for the environment's dynamic nature
   in use cases such as the one we present in this document.
   Consequently, the ABR algorithms perform sub-optimally in such
   deployments [ABR_1].

Krishna & Rahman           Expires May 3, 2021                  [Page 2]

Internet-Draft              MOPS AR Use Case                October 2020

2.  Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].

3.  Use Case

   We now descibe a use case that involves an application with AR
   systems' characteristics.  Consider a group of tourists who are being
   conducted in a tour around the historical site of the Tower of
   London.  As they move around the site and within the historical
   buildings, they can watch and listen to historical scenes in 3D that
   are generated by the AR application and then overlaid by their AR
   headsets onto their real-world view.  The headset then continuously
   updates their view as they move around.

   The AR application processes the scene that the walking tourist is
   watching in real-time and identifies objects that will be targeted
   for overlay of high resolution videos.  It then generates high
   resolution 3D images of historical scenes related to the perspective
   of the tourist in real-time.  These generated video images are then
   overlaid on the view of the real-world as seen by the tourist.

   Offloading to the remote Cloud is not feasible for applications with
   AR characteristics as the end-to-end delays must be within the order
   of a few milliseconds.  In order to achieve such hard timing
   constraints, computationally intensive tasks can be offloaded to Edge

4.  Requirements

   As discussed above an AR application requires offloading of its
   components to resources provided by Edge Computing.  These components
   perform tasks such as real-time generation and processing of high-
   quality video content that are too computationally intensive for the
   mobile device.

   In addition, such applications require high bandwidth and low jitter
   to provide a high QoE to the user.  Another consequence of running
   such computationally intensive applications on AR devices such as AR
   glasses is the excessive heat generated by the chip-sets that are
   involved in the computation [DEV_HEAT_1].  Finally, the battery on
   such devices discharges quickly when running such applications if
   some processing is not off-loaded to the Edge Computing.

   Note that the Edge device providing the computation and storage is
   itself limited in such resources compared to the Cloud.  So, for

Krishna & Rahman           Expires May 3, 2021                  [Page 3]

Internet-Draft              MOPS AR Use Case                October 2020

   example, a sudden surge in demand from a large group of tourists can
   overwhelm that device.  This will result in a degraded user
   experience as their AR device experiences delays in receiving the
   video frames.  In order to deal with this problem, the client AR
   applications will need to use Adaptive Bit Rate (ABR) algorithms that
   choose bit-rates policies tailored in a fine-grained manner to the
   resource demands and playback the videos with appropriate QoE metrics
   as the user moves around with the group of tourists.

   However, heavy-tailed nature of several operational parameters make
   prediction-based adaptation by ABR algorithms sub-optimal[ABR_2].
   This is because with such distributions, law of large numbers works
   too slowly, the mean of sample does not equal the mean of
   distribution, and as a result standard deviation and variance are
   unsuitable as metrics for such operational parameters [HEAVY_TAIL_1],
   [HEAVY_TAIL_2].  Other subtle issues with these distributions include
   the "expectation paradox" [HEAVY_TAIL_1] where the longer we have
   waited for an event the longer we have to wait and the issue of
   mismatch between the size and count of events [HEAVY_TAIL_1].  This
   makes designing an algorithm for adaptation error-prone and
   challenging.  Such operational parameters include but are not limited
   to buffer occupancy, throughput, client-server latency, and variable
   transmission times.In addition, edge devices and communication links
   may fail and logical communication relationships between various
   software components change frequently as the user moves around with
   their AR device [UBICOMP].

   Thus, once the offloaded computationally intensive processing is
   completed on the Edge Computing, the video is streamed to the user
   with the help of an ABR algorithm which needs to meet the following
   requirements [ABR_1]:

   o  Dynamically changing ABR parameters: The ABR algorithm must be
      able to dynamically change parameters given the heavy-tailed
      nature of network throughput.  This, for example, may be
      accomplished by AI/ML processing on the Edge Computing on a per
      client or global basis.

   o  Handling conflicting QoE requirements: QoE goals often require
      high bit-rates, and low frequency of buffer refills.  However in
      practice, this can lead to a conflict between those goals.  For
      example, increasing the bit-rate might result in the need to fill
      up the buffer more frequently as the buffer capacity might be
      limited on the AR device.  The ABR algorithm must be able to
      handle this situation.

   o  Handling side effects of deciding a specific bit rate: For
      example, selecting a bit rate of a particular value might result

Krishna & Rahman           Expires May 3, 2021                  [Page 4]

Internet-Draft              MOPS AR Use Case                October 2020

      in the ABR algorithm not changing to a different rate so as to
      ensure a non-fluctuating bit-rate and the resultant smoothness of
      video quality . The ABR algorithm must be able to handle this

5.  Informative References

   [ABR_1]    Mao, H., Netravali, R., and M. Alizadeh, "Neural Adaptive
              Video Streaming with Pensieve", In  Proceedings of the
              Conference of the ACM Special Interest Group on Data
              Communication, (pp. 197-210), 2017.

   [ABR_2]    Yan, F., Ayers, H., Zhu, C., Fouladi, S., Hong, J., Zhang,
              K., Levis, P., and K. Winstein, "Learning in situ: a
              randomized experiment in video streaming", In   17th
              {USENIX} Symposium on Networked Systems Design and
              Implementation ({NSDI} 20), (pp. 495-511), 2020.

              LiKamWa, R., Wang, Z., Carroll, A., Lin, F., and L. Zhong,
              "Draining our Glass: An Energy and Heat characterization
              of Google Glass", In Proceedings of 5th Asia-Pacific
              Workshop on Systems (pp. 1-7), 2013.

   [EDGE_1]   Satyanarayanan, M., "The Emergence of Edge Computing",
              In  Computer 50(1) (pp. 30-39), 2017.

   [EDGE_2]   Satyanarayanan, M., Klas, G., Silva, M., and S. Mangiante,
              "The Seminal Role of Edge-Native Applications", In  IEEE
              International Conference on Edge Computing (EDGE) (pp.
              33-40), 2019.

              Crovella, M. and B. Krishnamurthy, "Internet measurement:
              infrastructure, traffic and applications", John  Wiley and
              Sons Inc., 2006.

              Taleb, N., "The Statistical Consequences of Fat Tails",
              STEM  Academic Press, 2020.

              Holland, J., Begen, A., and S. Dawkins, "Operational
              Considerations for Streaming Media", draft-ietf-mops-
              streaming-opcons-02 (work in progress), July 2020.

Krishna & Rahman           Expires May 3, 2021                  [Page 5]

Internet-Draft              MOPS AR Use Case                October 2020

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

   [UBICOMP]  Bardram, J. and A. Friday, "Ubiquitous Computing Systems",
              In   Ubiquitous Computing Fundamentals (pp. 37-94). CRC
              Press, 2009.

Authors' Addresses

   Renan Krishna
   InterDigital Europe Limited
   64, Great Eastern Street
   London  EC2A 3QR
   United Kingdom


   Akbar Rahman
   InterDigital Communications, LLC
   1000 Sherbrooke Street West
   Montreal  H3A 3G4


Krishna & Rahman           Expires May 3, 2021                  [Page 6]