Media Operations Use Case for an Augmented Reality Application on Edge Computing Infrastructure
draft-krishna-mops-ar-use-case-01

Versions: 00 01                                                         
MOPS                                                          R. Krishna
Internet-Draft                               InterDigital Europe Limited
Intended status: Informational                                 A. Rahman
Expires: January 10, 2021               InterDigital Communications, LLC
                                                            July 9, 2020


 Media Operations Use Case for an Augmented Reality Application on Edge
                        Computing Infrastructure
                   draft-krishna-mops-ar-use-case-00

Abstract

   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.

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Table of Contents

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

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, it does not cover the increasingly large
   number of applications with Augmented Reality (AR) characteristics
   and their requirements on ABR algorithms.

   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].








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2.  Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119].

3.  Use Case

   A use case that considers an application with AR systems'
   characteristics is now described where a group of tourists 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
   devices.

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



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   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.

   Thus, once the offloaded computationally intensive processing is
   completed on the Edge Computing, the video is streamed to the user
   using an optimal ABR algorithm.  This imposes the following
   requirements on the ABR algorithm [ABR_1]:

   o  Dynamically changing ABR parameters: The ABR algorithm must be
      able to dynamically change parameters given the fat-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
      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
      situation.

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.

   [DEV_HEAT_1]
              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.




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   [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.

   [I-D.ietf-mops-streaming-opcons]
              Holland, J., Begen, A., and S. Dawkins, "Operational
              Considerations for Streaming Media", draft-ietf-mops-
              streaming-opcons-01 (work in progress), March 2020.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

Authors' Addresses

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

   Email: renan.krishna@interdigital.com


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

   Email: Akbar.Rahman@InterDigital.com















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