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Rahman 5 Expires: January 10, 2021 InterDigital Communications, LLC 6 July 9, 2020 8 Media Operations Use Case for an Augmented Reality Application on Edge 9 Computing Infrastructure 10 draft-krishna-mops-ar-use-case-00 12 Abstract 14 A use case describing transmission of an application on the Internet 15 that has several unique characteristics of Augmented Reality (AR) 16 applications is presented for the consideration of the Media 17 Operations (MOPS) Working Group. One key requirement identified is 18 that the Adaptive-Bit-Rate (ABR) algorithms' current usage of 19 policies based on heuristics and models is inadequate for AR 20 applications running on the Edge Computing infrastructure. 22 Status of This Memo 24 This Internet-Draft is submitted in full conformance with the 25 provisions of BCP 78 and BCP 79. 27 Internet-Drafts are working documents of the Internet Engineering 28 Task Force (IETF). Note that other groups may also distribute 29 working documents as Internet-Drafts. The list of current Internet- 30 Drafts is at https://datatracker.ietf.org/drafts/current/. 32 Internet-Drafts are draft documents valid for a maximum of six months 33 and may be updated, replaced, or obsoleted by other documents at any 34 time. It is inappropriate to use Internet-Drafts as reference 35 material or to cite them other than as "work in progress." 37 This Internet-Draft will expire on January 10, 2021. 39 Copyright Notice 41 Copyright (c) 2020 IETF Trust and the persons identified as the 42 document authors. All rights reserved. 44 This document is subject to BCP 78 and the IETF Trust's Legal 45 Provisions Relating to IETF Documents 46 (https://trustee.ietf.org/license-info) in effect on the date of 47 publication of this document. Please review these documents 48 carefully, as they describe your rights and restrictions with respect 49 to this document. Code Components extracted from this document must 50 include Simplified BSD License text as described in Section 4.e of 51 the Trust Legal Provisions and are provided without warranty as 52 described in the Simplified BSD License. 54 Table of Contents 56 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 57 2. Conventions used in this document . . . . . . . . . . . . . . 3 58 3. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 3 59 4. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 3 60 5. Informative References . . . . . . . . . . . . . . . . . . . 4 61 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 5 63 1. Introduction 65 The MOPS draft, [I-D.ietf-mops-streaming-opcons], provides an 66 overview of operational networking issues that pertain to Quality of 67 Experience (QoE) in delivery of video and other high-bitrate media 68 over the Internet. However, it does not cover the increasingly large 69 number of applications with Augmented Reality (AR) characteristics 70 and their requirements on ABR algorithms. 72 Future AR applications will bring several requirements for the 73 Internet and the mobile devices running these applications. AR 74 applications require a real-time processing of video streams to 75 recognize specific objects. This is then used to overlay information 76 on the video being displayed to the user. In addition some AR 77 applications will also require generation of new video frames to be 78 played to the user. In order to run future applications with AR 79 characteristics on mobile devices, computationally intensive tasks 80 need to be offloaded to resources provided by Edge Computing. 82 Edge Computing is an emerging paradigm where computing resources and 83 storage are made available in close network proximity at the edge of 84 the Internet to mobile devices and sensors [EDGE_1], [EDGE_2]. 86 Adaptive-Bit-Rate (ABR) algorithms currently base their policy for 87 bit-rate selection on heuristics or models of the deployment 88 environment that do not account for the environment's dynamic nature 89 in use cases such as the one we present in this document. 90 Consequently, the ABR algorithms perform sub-optimally in such 91 deployments [ABR_1]. 93 2. Conventions used in this document 95 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 96 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 97 document are to be interpreted as described in [RFC2119]. 99 3. Use Case 101 A use case that considers an application with AR systems' 102 characteristics is now described where a group of tourists are being 103 conducted in a tour around the historical site of the Tower of 104 London. As they move around the site and within the historical 105 buildings, they can watch and listen to historical scenes in 3D that 106 are generated by the AR application and then overlaid by their AR 107 headsets onto their real-world view. The headset then continuously 108 updates their view as they move around. 110 The AR application processes the scene that the walking tourist is 111 watching in real-time and identifies objects that will be targeted 112 for overlay of high resolution videos. It then generates high 113 resolution 3D images of historical scenes related to the perspective 114 of the tourist in real-time. These generated video images are then 115 overlaid on the view of the real-world as seen by the tourist. 117 Offloading to the remote Cloud is not feasible for applications with 118 AR characteristics as the end-to-end delays must be within the order 119 of a few milliseconds. In order to achieve such hard timing 120 constraints, computationally intensive tasks can be offloaded to Edge 121 devices. 123 4. Requirements 125 As discussed above an AR application requires offloading of its 126 components to resources provided by Edge Computing. These components 127 perform tasks such as real-time generation and processing of high- 128 quality video content that are too computationally intensive for the 129 mobile device. 131 In addition, such applications require high bandwidth and low jitter 132 to provide a high QoE to the user. Another consequence of running 133 such computationally intensive applications on AR devices such as AR 134 glasses is the excessive heat generated by the chip-sets that are 135 involved in the computation [DEV_HEAT_1]. Finally, the battery on 136 such devices discharges quickly when running such applications if 137 some processing is not off-loaded to the Edge Computing. 139 Note that the Edge device providing the computation and storage is 140 itself limited in such resources compared to the Cloud. So, for 141 example, a sudden surge in demand from a large group of tourists can 142 overwhelm that device. This will result in a degraded user 143 experience as their AR device experiences delays in receiving the 144 video frames. In order to deal with this problem, the client AR 145 applications will need to use Adaptive Bit Rate (ABR) algorithms that 146 choose bit-rates policies tailored in a fine-grained manner to the 147 resource demands and playback the videos with appropriate QoE metrics 148 as the user moves around with the group of tourists. 150 Thus, once the offloaded computationally intensive processing is 151 completed on the Edge Computing, the video is streamed to the user 152 using an optimal ABR algorithm. This imposes the following 153 requirements on the ABR algorithm [ABR_1]: 155 o Dynamically changing ABR parameters: The ABR algorithm must be 156 able to dynamically change parameters given the fat-tailed nature 157 of network throughput. This, for example, may be accomplished by 158 AI/ML processing on the Edge Computing on a per client or global 159 basis. 161 o Handling conflicting QoE requirements: QoE goals often require 162 high bit-rates, and low frequency of buffer refills. However in 163 practice, this can lead to a conflict between those goals. For 164 example, increasing the bit-rate might result in the need to fill 165 up the buffer more frequently as the buffer capacity might be 166 limited on the AR device. The ABR algorithm must be able to 167 handle this situation. 169 o Handling side effects of deciding a specific bit rate: For 170 example, selecting a bit rate of a particular value might result 171 in the ABR algorithm not changing to a different rate so as to 172 ensure a non-fluctuating bit-rate and the resultant smoothness of 173 video quality . The ABR algorithm must be able to handle this 174 situation. 176 5. Informative References 178 [ABR_1] Mao, H., Netravali, R., and M. Alizadeh, "Neural Adaptive 179 Video Streaming with Pensieve", In Proceedings of the 180 Conference of the ACM Special Interest Group on Data 181 Communication, (pp. 197-210), 2017. 183 [DEV_HEAT_1] 184 LiKamWa, R., Wang, Z., Carroll, A., Lin, F., and L. Zhong, 185 "Draining our Glass: An Energy and Heat characterization 186 of Google Glass", In Proceedings of 5th Asia-Pacific 187 Workshop on Systems (pp. 1-7), 2013. 189 [EDGE_1] Satyanarayanan, M., "The Emergence of Edge Computing", 190 In Computer 50(1) (pp. 30-39), 2017. 192 [EDGE_2] Satyanarayanan, M., Klas, G., Silva, M., and S. Mangiante, 193 "The Seminal Role of Edge-Native Applications", In IEEE 194 International Conference on Edge Computing (EDGE) (pp. 195 33-40), 2019. 197 [I-D.ietf-mops-streaming-opcons] 198 Holland, J., Begen, A., and S. Dawkins, "Operational 199 Considerations for Streaming Media", draft-ietf-mops- 200 streaming-opcons-01 (work in progress), March 2020. 202 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate 203 Requirement Levels", BCP 14, RFC 2119, 204 DOI 10.17487/RFC2119, March 1997, 205 . 207 Authors' Addresses 209 Renan Krishna 210 InterDigital Europe Limited 211 64, Great Eastern Street 212 London EC2A 3QR 213 United Kingdom 215 Email: renan.krishna@interdigital.com 217 Akbar Rahman 218 InterDigital Communications, LLC 219 1000 Sherbrooke Street West 220 Montreal H3A 3G4 221 Canada 223 Email: Akbar.Rahman@InterDigital.com