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Media Operations Use Case for an Extended Reality Application on Edge Computing Infrastructure
draft-ietf-mops-ar-use-case-15

Document Type Active Internet-Draft (mops WG)
Authors Renan Krishna , Akbar Rahman
Last updated 2024-03-04
Replaces draft-krishna-mops-ar-use-case
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Mar 2021
Initial draft operational considerations for low latency streaming video applications
Feb 2022
Revised draft operational considerations for low latency streaming video applications
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draft-ietf-mops-ar-use-case-15
MOPS                                                          R. Krishna
Internet-Draft                               InterDigital Europe Limited
Intended status: Informational                                 A. Rahman
Expires: 5 September 2024                                       Ericsson
                                                            4 March 2024

 Media Operations Use Case for an Extended Reality Application on Edge
                        Computing Infrastructure
                     draft-ietf-mops-ar-use-case-15

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, this document
   discusses those applications that run on devices having different
   form factors (such as different physical sizes and shapes) 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.  This document discusses the
   expected behavior of XR applications which can be used to manage the
   traffic.  In addition, the document discusses 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
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   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 5 September 2024.

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Copyright Notice

   Copyright (c) 2024 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 (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
   and restrictions with respect to this document.  Code Components
   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.  Use Case  . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Processing of Scenes  . . . . . . . . . . . . . . . . . .   4
     2.2.  Generation of Images  . . . . . . . . . . . . . . . . . .   5
   3.  Technical Challenges and Solutions  . . . . . . . . . . . . .   5
   4.  XR Network Traffic  . . . . . . . . . . . . . . . . . . . . .   7
     4.1.  Traffic Workload  . . . . . . . . . . . . . . . . . . . .   7
     4.2.  Traffic Performance Metrics . . . . . . . . . . . . . . .   8
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  10
   8.  Informative References  . . . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   Extended Reality (XR) is a term that includes Augmented Reality (AR),
   Virtual Reality (VR) and Mixed Reality (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].

   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

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   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 (using SDN to break vertically integrated systems into
   independent components- these components can have open interfaces
   which are standard, well documented and not proprietary),
   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.  This is
   because the disaggregated components can run on proximate edge
   devices rather than on remote cloud several hops away and deliver low
   latency, high bandwidth service to offloaded applications [EDGE_2].

   This document discusses 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 location
   sensing capabilities using systems such as a global navigation
   satellite system (GNSS) 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.  Heaviness of tails is defined as the difference
   from the normal distribution in the proportion of the values that

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   fall a long way from the mean [HEAVY_TAIL_3].  Such workloads require
   appropriate resource management policies to be used on the Edge.  The
   service requirements of XR applications are also challenging when
   compared to the current video applications.  In particular several
   Quality of Experience (QoE) factors such as motion sickness are
   unique to XR applications and must be considered when
   operationalizing a network.  This document motivates these issues
   with a use-case that is presented in the following sections.

2.  Use Case

   A use case is now described that involves an application with XR
   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 XR application and then overlaid by their XR
   headsets onto their real-world view.  The headset then continuously
   updates their view as they move around.

   The XR 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.

   This processing of scenes and generation of high-resolution images is
   now discussed in greater detail.

2.1.  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 XR 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.

   Acquisition of a model of the real world: The tracked natural
   features are used to develop an annotated point cloud (a set of
   points in space that are annotated with descriptors) based model that
   is then stored in a database.  To ensure that this database can be

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   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.  Such a 2D lightfield is stored as a two-dimensional
   table.  Assuming distant light sources, the single point is
   approximately valid for small scenes.  For larger scenes, many 3D
   positions are additionally stored making the table 5D [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 XR 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.

2.2.  Generation of Images

   The XR application must generate a high-quality video that has the
   properties described in the previous step and overlay the video on
   the XR 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.

3.  Technical Challenges and Solutions

   The components of XR applications perform tasks such as real-time
   generation and processing of high-quality video content that are
   computationally intensive.  As a result, on XR devices such as XR
   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].

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   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 (streaming of video that represents a view in
   every direction in 3D space) is that the display on the XR 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 round-trip-time (RTT) between the XR
   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, a 3GPP study indicates an Ultra
   Reliable Low Latency of 0.1ms to 1ms for communication between an
   Edge server and User Equipment (UE) [URLLC].

   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 XR device experiences delays in receiving the
   video frames.  In order to deal with this problem, the client XR
   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 [HEAVY_TAIL_2], the mean of sample does not equal the mean
   of distribution [HEAVY_TAIL_2], and as a result standard deviation
   and variance are unsuitable as metrics for such operational

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   parameters [HEAVY_TAIL_1].  Other subtle issues with these
   distributions include the "expectation paradox" [HEAVY_TAIL_1] where
   the longer the wait for an event, the longer a further need 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 XR device [UBICOMP].

4.  XR Network Traffic

4.1.  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 user 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 6
   degrees of freedom (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 [HEAVY_TAIL_1].
   Self-similar traffic contains bursts at a wide range of time scales
   [HEAVY_TAIL_1].  Multi-fractal spectrum bursts for traffic summarizes
   the statistical distribution of local scaling exponents found in a
   traffic trace [HEAVY_TAIL_1].  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

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   are connected wirelessly could face long bursts of traffic
   [METRICS_2], [METRICS_3].  In addition, multi-fractal spectrum
   burstiness at the scale of milli-seconds could induce jitter
   contributing to motion sickness [METRICS_4].  This is because bursty
   traffic combined with variable queueing delays leads to large delay
   jitter [METRICS_4].  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 placement of XR servers,
   mobility support and energy management [METRICS_6].  Providing Edge
   server support for the techniques being developed at the DETNET
   Working Group 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.

4.2.  Traffic Performance Metrics

   The performance requirements for XR traffic have characteristics that
   need to be considered when operationalizing a network.  These
   characteristics are now discussed.

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

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

   *  The uploading of data from an XR 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 XR device uploads additional visual
      data for processing to the remote server.

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

   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 required response times and bandwidths.
   Response times can be defined as the time interval between the end of
   a request submission and the end of the corresponding response from a
   system.  If the XR device offloads a task to an edge server, the
   response time of the server is the round trip time from when a data
   packet is sent from the XR device until a response is received.  Note
   that the required response time provides an upper bound on the sum of
   the time taken by computational tasks such as processing of scenes,
   generation of images and the round-trip time.  This response time
   depends only on the Quality of Service (QOS) required by an
   application.  The response time is therefore independent of the
   underlying technology of the network and the time taken by the
   computational tasks.

   Our use case requires a response time of 20ms at most and preferably
   between 7-15ms as discussed earlier.  The required bandwidth for our
   use case as discussed in section 5.1, Table 1, 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       | Required     | Expected | Possible            |
   |                   | Response     | Data     | Implementations/    |
   |                   | Time         | Capacity | Examples            |
   +===================+==============+==========+=====================+
   | Mobile XR 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 XR         | milliseconds | 100 Mbps | XR-enhanced         |
   | applications      |              |          | Google Translate    |
   +-------------------+--------------+----------+---------------------+

      Table 2: Traffic Performance Metrics of Selected XR Applications

5.  IANA Considerations

   This document has no IANA actions.

6.  Security Considerations

   The security issues for the presented use case are similar to other
   streaming applications.  This document itself introduces no new
   security issues.

7.  Acknowledgements

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

8.  Informative References

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   [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.

   [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.

   [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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   [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.

   [EDGE_3]   Peterson, L. and O. Sunay, "5G mobile networks: A systems
              approach.", In Synthesis Lectures on Network Systems.,
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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

   Akbar Rahman
   Ericsson
   349 Terry Fox Drive
   Ottawa Ontario  K2K 2V6
   Canada
   Email: Akbar.Rahman@ericsson.com

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