MOPS                                                          R. Krishna
Internet-Draft                               InterDigital Europe Limited
Intended status: Informational                                 A. Rahman
Expires: April 28, 2022                 InterDigital Communications, LLC
                                                        October 25, 2021

 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.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions used in this document . . . . . . . . . . . . . .   3
   3.  Use Case  . . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Processing of Scenes  . . . . . . . . . . . . . . . . . .   3
     3.2.  Generation of Images  . . . . . . . . . . . . . . . . . .   4
   4.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   4
   5.  AR Network Traffic and Interaction with TCP . . . . . . . . .   6
   6.  Informative References  . . . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

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.  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 AR mobile device.
   Consequently, 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

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   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].

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 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

   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.

   Acquisition of a model of the real world: The tracked natural
   features are used to develop an annotated point cloud based model

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   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].

   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.

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.

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   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].

   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

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   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
      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.  AR Network Traffic and Interaction with TCP

   In addition to the requirements for ABR algorithms, there are other
   operational issues that need to be considered for AR use cases such
   as the one descibed above.  In a study [AR_TRAFFIC] conducted to
   characterize multi-user AR over cellular networks, the following
   issues were identified:

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

   o  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.

   o  AR applications tend to have large bursts that are separated by
      significant time gaps.  As a result, the TCP congestion window
      enters slow start before the large bursts of data arrive
      increasing the perceived user latency.  The study [AR_TRAFFIC]

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      shows that segmentation latency at 4G LTE (Long Term Evolution)'s
      RAN (Radio Access Network)'s RLC (Radio Link Control) layer
      impacts TCP's performance during slow-start.

6.  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.

              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.

              Schmalstieg, D. and T. Hollerer, "Augmented
              Reality",  Addison Wesley, 2016.

              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.,

   [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.

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              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.

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

   [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.

              Kan, P. and H. Kaufmann, "Differential irradiance caching
              for fast high-quality light transport between virtual and
              real worlds.", In  IEEE International Symposium on Mixed
              and Augmented Reality (ISMAR),pp. 133-141, 2013.

              Franke, T., "Delta voxel cone tracing.", In  IEEE
              International Symposium on Mixed and Augmented Reality
              (ISMAR), pp. 39-44, 2014.

              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-07 (work in progress), September 2021.

              Fuhrmann, A. and D. Schmalstieg, "Practical calibration
              procedures for augmented reality.", In  Virtual
              Environments 2000, pp. 3-12. Springer, Vienna, 2000.

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   [NOISE]    Fischer, J., Bartz, D., and W. Strasser, "Enhanced visual
              realism by incorporating camera image effects.",
              In  IEEE/ACM International Symposium on Mixed and
              Augmented Reality, pp. 205-208., 2006.

   [OCCL_1]   Breen, D., Whitaker, R., and M. Tuceryan, "Interactive
              Occlusion and automatic object placementfor augmented
              reality", In  Computer Graphics Forum, vol. 15, no. 3 ,
              pp. 229-238,Edinburgh, UK: Blackwell Science Ltd, 1996.

   [OCCL_2]   Zheng, F., Schmalstieg, D., and G. Welch, "Pixel-wise
              closed-loop registration in video-based augmented
              reality", In  IEEE International Symposium on Mixed and
              Augmented Reality (ISMAR), pp. 135-143, 2014.

   [OCCL_3]   Lang, B., "Oculus Shares 5 Key Ingredients for Presence in
              Virtual Reality.",

              Mania, K., Adelstein, B., Ellis, S., and M. Hill,
              "Perceptual sensitivity to head tracking latency in
              virtual environments with varying degrees of scene
              complexity.", In  Proceedings of the 1st Symposium on
              Applied perception in graphics and visualization pp.
              39-47., 2004.

              Liu, Y. and X. Granier, "Online tracking of outdoor
              lighting variations for augmented reality with moving
              cameras", In  IEEE Transactions on visualization and
              computer graphics, 18(4), pp.573-580, 2012.

   [PREDICT]  Buker, T., Vincenzi, D., and J. Deaton, "The effect of
              apparent latency on simulator sickness while using a see-
              through helmet-mounted display: Reducing apparent latency
              with predictive compensation..", In  Human factors 54.2,
              pp. 235-249., 2012.

   [REG]      Holloway, R., "Registration error analysis for augmented
              reality.", In  Presence:Teleoperators and Virtual
              Environments 6.4, pp. 413-432., 1997.

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

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   [SLAM_1]   Ventura, J., Arth, C., Reitmayr, G., and D. Schmalstieg,
              "A minimal solution to the generalized pose-and-scale
              problem", In  Proceedings of the IEEE Conference on
              Computer Vision and Pattern Recognition, pp. 422-429,

   [SLAM_2]   Sweeny, C., Fragoso, V., Hollerer, T., and M. Turk, "A
              scalable solution to the generalized pose and scale
              problem", In  European Conference on Computer Vision, pp.
              16-31, 2014.

   [SLAM_3]   Gauglitz, S., Sweeny, C., Ventura, J., Turk, M., and T.
              Hollerer, "Model estimation and selection towards
              unconstrained real-time tracking and mapping", In  IEEE
              transactions on visualization and computer graphics,
              20(6), pp. 825-838, 2013.

   [SLAM_4]   Pirchheim, C., Schmalstieg, D., and G. Reitmayr, "Handling
              pure camera rotation in keyframe-based SLAM", In  2013
              IEEE international symposium on mixed and augmented
              reality (ISMAR), pp. 229-238, 2013.

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

   [URLLC]    3GPP, "3GPP TR 23.725: Study on enhancement of Ultra-
              Reliable Low-Latency Communication (URLLC) support in the
              5G Core network (5GC).",
              SpecificationDetails.aspx?specificationId=3453, 2019.

              Kalkofen, D., Mendez, E., and D. Schmalstieg, "Interactive
              focus and context visualization for augmented reality.",
              In  6th IEEE and ACM International Symposium on Mixed and
              Augmented Reality, pp. 191-201., 2007.

   [XR]       3GPP, "3GPP TR 26.928: Extended Reality (XR) in 5G.",
              SpecificationDetails.aspx?specificationId=3534, 2020.

Authors' Addresses

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   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


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