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Media Operations Use Case for an Extended Reality Application on Edge Computing Infrastructure
draft-ietf-mops-ar-use-case-10

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Authors Renan Krishna , Akbar Rahman
Last updated 2023-03-13
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draft-ietf-mops-ar-use-case-10
MOPS                                                          R. Krishna
Internet-Draft                               InterDigital Europe Limited
Intended status: Informational                                 A. Rahman
Expires: 14 September 2023                                      Ericsson
                                                           13 March 2023

 Media Operations Use Case for an Extended Reality Application on Edge
                        Computing Infrastructure
                     draft-ietf-mops-ar-use-case-10

Abstract

   This document explores the issues involved in the use of Edge
   Computing resources to operationalize media use cases that involve
   Extended Reality (XR) applications.  In particular, we discuss those
   applications that run on devices having different form factors and
   need Edge computing resources to mitigate the effect of problems such
   as a need to support interactive communication requiring low latency,
   limited battery power, and heat dissipation from those devices.  The
   intended audience for this document are network operators who are
   interested in providing edge computing resources to operationalize
   the requirements of such applications.  We discuss the expected
   behavior of XR applications which can be used to manage the traffic.
   In addition, we discuss the service requirements of XR applications
   to be able to run on the network.

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
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   This Internet-Draft will expire on 14 September 2023.

Copyright Notice

   Copyright (c) 2023 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
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   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions used in this document . . . . . . . . . . . . . .   4
   3.  Use Case  . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Processing of Scenes  . . . . . . . . . . . . . . . . . .   4
     3.2.  Generation of Images  . . . . . . . . . . . . . . . . . .   5
   4.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   6
   5.  AR Network Traffic  . . . . . . . . . . . . . . . . . . . . .   8
     5.1.  Traffic Workload  . . . . . . . . . . . . . . . . . . . .   8
     5.2.  Traffic Performance Metrics . . . . . . . . . . . . . . .   9
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  11
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

1.  Introduction

   Extended Reality (XR) is a term that includes Augmented Realty (AR),
   Virtual Reality (VR) and Mixed Realty (MR) [XR].  AR combines the
   real and virtual, is interactive and is aligned to the physical world
   of the user [AUGMENTED_2].  On the other hand, VR places the user
   inside a virtual environment generated by a computer [AUGMENTED].MR
   merges the real and virtual world along a continuum that connects
   completely real environment at one end to a completely virtual
   environment at the other end.  In this continuum, all combinations of
   the real and virtual are captured [AUGMENTED].

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   XR applications will bring several requirements for the network and
   the mobile devices running these applications.  Some XR applications
   such as AR 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 XR
   applications such as AR and VR will also require generation of new
   video frames to be played to the user.  Both the real-time processing
   of video streams and the generation of overlay information are
   computationally intensive tasks that generate heat [DEV_HEAT_1],
   [DEV_HEAT_2] and drain battery power [BATT_DRAIN] on the mobile
   device running the XR application.  Consequently, in order to run
   applications with XR 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].  These
   edge computing devices use cloud technologies that enable them to
   support offloaded XR applications.  In particular, the edge devices
   deploy cloud computing implementation techniques such as
   disaggregation (breaking vertically integrated systems into
   independent components with open interfaces using SDN),
   virtualization (being able to run multiple independent copies of
   those components such as SDN Controller apps, Virtual Network
   Functions on a common hardware platform) and commoditization ( being
   able to elastically scale those virtual components across commodity
   hardware as the workload dictates) [EDGE_3].  Such techniques enable
   XR applications requiring low-latency and high bandwidth to be
   delivered by mini-clouds running on proximate edge devices

   In this document, we discuss the issues involved when edge computing
   resources are offered by network operators to operationalize the
   requirements of XR applications running on devices with various form
   factors.  Examples of such form factors include Head Mounted Displays
   (HMD) such as Optical-see through HMDs and video-see-through HMDs and
   Hand-held displays.  Smart phones with video cameras and GPS are
   another example of such devices.  These devices have limited battery
   capacity and dissipate heat when running.  Besides as the user of
   these devices moves around as they run the XR application, the
   wireless latency and bandwidth available to the devices fluctuates
   and the communication link itself might fail.  As a result algorithms
   such as those based on adaptive-bit-rate techniques that base their
   policy on heuristics or models of deployment perform sub-optimally in
   such dynamic environments[ABR_1].  In addition, network operators can
   expect that the parameters that characterize the expected behavior of
   XR applications are heavy-tailed.  Such workloads require appropriate
   resource management policies to be used on the Edge.  The service

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   requirements of XR applications are also challenging when compared to
   the current video applications.  In particular several QoE factors
   such as motion sickness are unique to XR applications and must be
   considered when operationalizing a network.  We motivate these issues
   with a use-case that we present in the following sections.

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

   We now describe 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 first 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.

   We now discuss this processing of scenes and generation of high
   resolution images in greater detail.

3.1.  Processing of Scenes

   The task of processing a scene can be broken down into a pipeline of
   three consecutive subtasks namely tracking, followed by an
   acquisition of a model of the real world, and finally registration
   [AUGMENTED].

   Tracking: This includes tracking of the three dimensional coordinates
   and six dimensional pose (coordinates and orientation) of objects in
   the real world[AUGMENTED].  The AR application that runs on the
   mobile device needs to track the pose of the user's head, eyes and
   the objects that are in view.This requires tracking natural features
   that are then used in the next stage of the pipeline.

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   Acquisition of a model of the real world: The tracked natural
   features are used to develop an annotated point cloud based model
   that is then stored in a database.To ensure that this database can be
   scaled up,techniques such as combining a client side simultaneous
   tracking and mapping and a server-side localization are used[SLAM_1],
   [SLAM_2], [SLAM_3], [SLAM_4].Another model that can be built is based
   on polygon mesh and texture mapping technique.  The polygon mesh
   encodes a 3D object's shape which is expressed as a collection of
   small flat surfaces that are polygons.  In texture mapping, color
   patterns are mapped on to an object's surface.  A third modelling
   technique uses a 2D lightfield that describes the intensity or color
   of the light rays arriving at a single point from arbitrary
   directions.  Assuming distant light sources, the single point is
   approximately valid for small scenes.  For larger scenes, a 5D
   lightfield is used which encodes seperate 2D lightfields for many 3D
   positions in space [AUGMENTED].

   Registration: The coordinate systems, brightness, and color of
   virtual and real objects need to be aligned in a process called
   registration [REG].  Once the natural features are tracked as
   discussed above, virtual objects are geometrically aligned with those
   features by geometric registration .This is followed by resolving
   occlusion that can occur between virtual and the real objects
   [OCCL_1], [OCCL_2].  The AR application also applies photometric
   registration [PHOTO_REG] by aligning the brightness and color between
   the virtual and real objects.Additionally, algorithms that calculate
   global illumination of both the virtual and real objects
   [GLB_ILLUM_1], [GLB_ILLUM_2] are executed.Various algorithms to deal
   with artifacts generated by lens distortion [LENS_DIST], blur [BLUR],
   noise [NOISE] etc are also required.

3.2.  Generation of Images

   The AR application must generate a high-quality video that has the
   properties described in the previous step and overlay the video on
   the AR device's display- a step called situated visualization.  This
   entails dealing with registration errors that may arise, ensuring
   that there is no visual interference [VIS_INTERFERE], and finally
   maintaining temporal coherence by adapting to the movement of user's
   eyes and head.

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4.  Requirements

   The components of AR applications perform tasks such as real-time
   generation and processing of high-quality video content that are
   computationally intensive.  As a result,on AR devices such as AR
   glasses excessive heat is generated by the chip-sets that are
   involved in the computation [DEV_HEAT_1], [DEV_HEAT_2].
   Additionally, the battery on such devices discharges quickly when
   running such applications [BATT_DRAIN].

   A solution to the heat dissipation and battery drainage problem is to
   offload the processing and video generation tasks to the remote
   cloud.However, running such tasks on the cloud is not feasible as the
   end-to-end delays must be within the order of a few milliseconds.
   Additionally,such applications require high bandwidth and low jitter
   to provide a high QoE to the user.In order to achieve such hard
   timing constraints, computationally intensive tasks can be offloaded
   to Edge devices.

   Another requirement for our use case and similar applications such as
   360 degree streaming is that the display on the AR/VR device should
   synchronize the visual input with the way the user is moving their
   head.  This synchronization is necessary to avoid motion sickness
   that results from a time-lag between when the user moves their head
   and when the appropriate video scene is rendered.  This time lag is
   often called "motion-to-photon" delay.  Studies have shown
   [PER_SENSE], [XR], [OCCL_3] that this delay can be at most 20ms and
   preferably between 7-15ms in order to avoid the motion sickness
   problem.  Out of these 20ms, display techniques including the refresh
   rate of write displays and pixel switching take 12-13ms [OCCL_3],
   [CLOUD].  This leaves 7-8ms for the processing of motion sensor
   inputs, graphic rendering, and RTT between the AR/VR device and the
   Edge.  The use of predictive techniques to mask latencies has been
   considered as a mitigating strategy to reduce motion sickness
   [PREDICT].  In addition, Edge Devices that are proximate to the user
   might be used to offload these computationally intensive tasks.
   Towards this end, the 3GPP requires and supports an Ultra Reliable
   Low Latency of 0.1ms to 1ms for communication between an Edge server
   and User Equipment(UE) [URLLC].

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

   *  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.

   *  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.

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   *  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.  AR Network Traffic

5.1.  Traffic Workload

   As discussed earlier, the parameters that capture the characteristics
   of XR application behavior are heavy-tailed.  Examples of such
   parameters include the distribution of arrival times between XR
   application invocation, the amount of data transferred, and the
   inter-arrival times of packets within a session.As a result, any
   traffic model based on such parameters are themselves heavy-tailed.
   Using these models to predict performance under alternative resource
   allocations by the network operator is challenging.  For example,
   both uplink and downlink traffic to a UE device has parameters such
   as volume of XR data, burst time, and idle time that are heavy
   tailed.

   Table 1 below shows various XR applications and their associated
   throughput requirements [METRICS_1].  Our use case envisages a 6DoF
   video or point cloud and so will require 200 to 1000Mbps of
   bandwidth.  As seen from the table, the XR application such as our
   use case transmit a larger amount of data per unit time as compared
   to traditional video applications.  As a result, issues arising out
   of heavy tailed parameters such as long-range dependent traffic
   [METRICS_2], self-similar traffic [METRICS_3], would be experienced
   at time scales of milliseconds and microseconds rather than hours or
   seconds.  Additionally, burstiness at the time scale of tens of
   milliseconds due to multi-fractal spectrum of traffic will be
   experienced [METRICS_4].  Long-range dependent traffic can have long
   bursts and various traffic parameters from widely separated time can
   show correlation.  Self-similar traffic contains bursts at a wide
   range of time scales.  Multi-fractal spectrum bursts for traffic
   summarizes the statistical distribution of local scaling exponents
   found in a traffic trace.  The operational consequences of XR traffic
   having characteristics such as long-range dependency, and self-
   similarity is that the edge servers to which multiple XR devices are
   connected wirelessly could face long bursts of traffic.  In addition,
   multi-fractal spectrum burstiness at the scale of milli-seconds could
   induce jitter contributing to motion sickness.  The operators of edge
   servers will need to run a 'managed edge cloud service' [METRICS_5]
   to deal with the above problems.  Functionalities that such a managed
   edge cloud service could operationally provide include dynamic

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   placement of XR servers, mobility support and energy management
   [METRICS_6].  Providing Edge server support for the techniques being
   developed at the DETNET and RAW Working Groups at the IETF could
   guarantee performance of XR applications.

        +===================================+=====================+
        | Application                       | Throughput Required |
        +===================================+=====================+
        | Image and Workflow Downloading    | 1 Mbps              |
        +-----------------------------------+---------------------+
        | Video Conferencing                | 2 Mbps              |
        +-----------------------------------+---------------------+
        | 3D Model and Data Visualization   | 2 to 20 Mbps        |
        +-----------------------------------+---------------------+
        | Two way Telepresence              | 5 to 25 Mbps        |
        +-----------------------------------+---------------------+
        | Current-Gen 360 degree video (4K) | 10 to 50 Mbps       |
        +-----------------------------------+---------------------+
        | Next-Gen 360 degree video (8K,    | 50 to 200 Mbps      |
        | 90+ FPS, HDR, Stereoscopic)       |                     |
        +-----------------------------------+---------------------+
        | 6DoF Video or Point Cloud         | 200 to 1000 Mbps    |
        +-----------------------------------+---------------------+

                Table 1: Throughput of some XR Applications

   Thus, the provisioning of edge servers in terms of the number of
   servers, the topology, where to place them, the assignment of link
   capacity, CPUs and GPUs should keep the above factors in mind.

5.2.  Traffic Performance Metrics

   The performance requirements for AR/VR traffic have characteristics
   that need to be considered when operationalizing a network.  We now
   discuss these characteristics.

   The bandwidth requirements of XR applications are substantially
   higher than those of video based applications.

   The latency requirements of XR applications have been studied
   recently [AR_TRAFFIC] .The following issues were identified.:

   *  The uploading of data from an AR device to a remote server for
      processing dominates the end-to-end latency.

   *  A lack of visual features in the grid environment can cause
      increased latencies as the AR device uploads additional visual
      data for processing to the remote server.

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   *  AR applications tend to have large bursts that are separated by
      significant time gaps.

   The packet loss rates in wireless links between XR devices and the
   Edge server can be as high as 2% or more [WIRELESS_1].

   Additionally, XR applications interact with each other on a time
   scale of a round-trip-time propagation and this must be considered
   when operationalizing a network.

   The following Table 2 [METRICS_6] shows a taxonomy of applications
   with their associated expected end-to-end latencies and bandwidths.
   Our use case requires an end-to-end latency of 20ms at most and
   preferably between 7-15ms as discussed earlier.  The required
   bandwidth for our use case as discussed in section 5.2 is 200Mbps-
   1000Mbps.  Since our use case envisages multiple users running the XR
   applications on their devices, and connected to an edge server that
   is closest to them, these latency and bandwidth connections will grow
   linearly with the number of users.  The operators should match the
   network provisioning to the maximum number of tourists that can be
   supported by a link to an edge server.

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   +===================+==============+==========+=====================+
   | Application       | Expected     | Expected | Possible            |
   |                   | End-To-End   | Data     | Implementations/    |
   |                   | Latency      | Latency  | Examples            |
   +===================+==============+==========+=====================+
   | AR-based remote   | Less than    | Greater  | World's first       |
   | surgery with      | 750          | than 30  | remote surgery      |
   | uncompressed 4K   | microseconds | Gbps     | over 5G             |
   | (3840x2160        |              |          |                     |
   | pixels) 120 fps   |              |          |                     |
   | HDR 10-bit real   |              |          |                     |
   | time video stream |              |          |                     |
   +-------------------+--------------+----------+---------------------+
   | Mobile AR based   | Less than 10 | Greater  | Assisting           |
   | remote assistance | milliseconds | than 7.5 | maintenance         |
   | with uncompressed |              | Gbps     | technicians,        |
   | 4K (1920x1080     |              |          | Industry 4.0        |
   | pixels) 120 fps   |              |          | remote              |
   | HDR 10-bit real-  |              |          | maintenance,        |
   | time video stream |              |          | remote assistance   |
   |                   |              |          | in robotics         |
   |                   |              |          | industry            |
   +-------------------+--------------+----------+---------------------+
   | Indoor and        | Less than 20 | 50 to    | Theme Parks,        |
   | localized outdoor | milliseconds | 200 Mbps | Shopping Malls,     |
   | navigation        |              |          | Archaeological      |
   |                   |              |          | Sites, Museum       |
   |                   |              |          | guidance            |
   +-------------------+--------------+----------+---------------------+
   | Cloud-based       | Less than 50 | 50 to    | Google Live View,   |
   | Mobile AR         | milliseconds | 100 Mbps | AR-enhanced         |
   | applications      |              |          | Google Translate    |
   +-------------------+--------------+----------+---------------------+

      Table 2: Traffic Performance Metrics of Selected XR Applications

6.  Acknowledgements

   Many Thanks to Spencer Dawkins, Rohit Abhishek, Jake Holland, Kiran
   Makhijani ,Ali Begen and Cullen Jennings for providing very helpful
   feedback suggestions and comments.

7.  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.

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   [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.

   [AR_TRAFFIC]
              Apicharttrisorn, K., Balasubramanian, B., Chen, J.,
              Sivaraj, R., Tsai, Y., Jana, R., Krishnamurthy, S., Tran,
              T., and Y. Zhou, "Characterization of Multi-User Augmented
              Reality over Cellular Networks", In 17th Annual IEEE
              International Conference on Sensing, Communication, and
              Networking (SECON), pp. 1-9. IEEE, 2020.

   [AUGMENTED]
              Schmalstieg, D. S. and T.H. Hollerer, "Augmented
              Reality",  Addison Wesley, 2016.

   [AUGMENTED_2]
              Azuma, R. T., "A Survey of Augmented
              Reality.",  Presence:Teleoperators and Virtual
              Environments 6.4, pp. 355-385., 1997.

   [BATT_DRAIN]
              Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S.,
              Thilakarathna, K., Hassan, M., and A. Seneviratne, "A
              survey of wearable devices and challenges.", In IEEE
              Communication Surveys and Tutorials, 19(4), p.2573-2620.,
              2017.

   [BLUR]     Kan, P. and H. Kaufmann, "Physically-Based Depth of Field
              in Augmented Reality.", In Eurographics (Short Papers),
              pp. 89-92., 2012.

   [CLOUD]    Corneo, L., Eder, M., Mohan, N., Zavodovski, A., Bayhan,
              S., Wong, W., Gunningberg, P., Kangasharju, J., and J.
              Ott, "Surrounded by the Clouds: A Comprehensive Cloud
              Reachability Study.", In Proceedings of the Web Conference
              2021, pp. 295-304, 2021.

   [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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   [DEV_HEAT_2]
              Matsuhashi, K., Kanamoto, T., and A. Kurokawa, "Thermal
              model and countermeasures for future smart glasses.",
              In Sensors, 20(5), p.1446., 2020.

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Authors' Addresses

   Renan Krishna
   InterDigital Europe Limited
   64, Great Eastern Street
   London
   EC2A 3QR
   United Kingdom
   Email: renan.krishna@interdigital.com

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   Akbar Rahman
   Ericsson
   8275 route Transcanadienne
   Montreal  H4S 0B6
   Canada
   Email: rahmansakbar@yahoo.com

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