Media Operations Use Case for an Extended Reality Application on Edge Computing Infrastructure
draft-ietf-mops-ar-use-case-18

MOPS                                                          R. Krishna
Internet-Draft                                                          
Intended status: Informational                                 A. Rahman
Expires: 21 December 2024                                       Ericsson
                                                            19 June 2024

 Media Operations Use Case for an Extended Reality Application on Edge
                        Computing Infrastructure
                     draft-ietf-mops-ar-use-case-18

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.

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   This Internet-Draft will expire on 21 December 2024.

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Copyright Notice

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Use Case  . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Processing of Scenes  . . . . . . . . . . . . . . . . . .   5
     2.2.  Generation of Images  . . . . . . . . . . . . . . . . . .   6
   3.  Technical Challenges and Solutions  . . . . . . . . . . . . .   6
   4.  XR Network Traffic  . . . . . . . . . . . . . . . . . . . . .   8
     4.1.  Traffic Workload  . . . . . . . . . . . . . . . . . . . .   8
     4.2.  Traffic Performance Metrics . . . . . . . . . . . . . . .   9
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  11
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  11
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  12
   9.  Informative References  . . . . . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  17

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

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   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 for the purpose of this
   document, 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].  A computing resource or storage is in
   close network proximity to a mobile device or sensor if there is a
   short and high-capacity network path to it such that the latency and
   bandwidth requirements of applications running on those mobile
   devices or sensors can be met.  These edge computing devices use
   cloud technologies that enable them to support offloaded XR
   applications.  In particular, cloud implementation techniques
   [EDGE_3] such as the follows can be deployed:

   *  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).

   *  Commoditization (being able to elastically scale those virtual
      components across commodity hardware as the workload dictates).

   Such techniques enable XR applications requiring low-latency and high
   bandwidth to be delivered by 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.  A network operator for the purposes of this document is any
   organization or individual that manages or operates the compute
   resources or storage in close network proximity to a mobile device or
   sensors.  Examples of form factors include Head Mounted Displays
   (HMD) such as Optical-see through HMDs and video-see-through HMDs and

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   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
   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.

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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: The XR application that runs on the mobile device needs to
   track the six-dimensional pose (translational in the three
   perpendicular axes and rotational about those three axes) of the
   user's head, eyes and the objects that are in view [AUGMENTED].  This
   requires tracking natural features (for example points or edges of
   objects) 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 a model of the real world.  One of the
   ways this is done is 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
   scaled up, techniques such as combining a client-side simultaneous
   tracking and mapping and a server-side localization are used to
   construct a model of the real world [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.  A set of all such points (either 2D or 5D
   lightfield) can then be used to construct a model of the real world
   [AUGMENTED].

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   Registration: The coordinate systems, brightness, and color of
   virtual and real objects need to be aligned with each other and this
   process is 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.  A
   situated visualization is a visualization in which the virtual
   objects that need to be seen by the XR user are overlaid correctly on
   the real world.  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

   As discussed in section 2, the components of XR applications perform
   tasks such as real-time generation and processing of high-quality
   video content that are computationally intensive.  This section will
   discuss the challenges such applications can face as a consequence.

   As a result of performing computationally intensive tasks 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].

   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 (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 makes
   prediction-based adaptation by ABR algorithms sub-optimal [ABR_2].
   This is because with such distributions, law of large numbers (how
   long does it take for sample mean to stabilize) 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 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

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   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 streaming video applications and their
   associated throughput requirements [METRICS_1].  Since our use case
   envisages a 6 degrees of freedom (6DoF) video or point cloud, it can
   be seen from the table that it 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 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

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   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 [RFC8939], [RFC9023], [RFC9450]
   could guarantee performance of XR applications.  For example, these
   techniques could be used for the link between the XR device and the
   edge as well as within the managed edge cloud service.  Another
   option for the network operators could be to deploy equipment that
   supports differentiated services [RFC2475] or per-connection quality-
   of-service guarantees [RFC2210].

      +===============================================+============+
      | Application                                   | Throughput |
      |                                               | Required   |
      +===============================================+============+
      | Real-world objects annotated with text and    | 1 Mbps     |
      | images for workflow assistance (e.g. repair)  |            |
      +-----------------------------------------------+------------+
      | Video Conferencing                            | 2 Mbps     |
      +-----------------------------------------------+------------+
      | 3D Model and Data Visualization               | 2 to 20    |
      |                                               | Mbps       |
      +-----------------------------------------------+------------+
      | Two-way 3D Telepresence                       | 5 to 25    |
      |                                               | Mbps       |
      +-----------------------------------------------+------------+
      | Current-Gen 360-degree video (4K)             | 10 to 50   |
      |                                               | Mbps       |
      +-----------------------------------------------+------------+
      | Next-Gen 360-degree video (8K, 90+ Frames-    | 50 to 200  |
      | per-second, High Dynamic Range, Stereoscopic) | Mbps       |
      +-----------------------------------------------+------------+
      | 6 Degree of Freedom Video or Point Cloud      | 200 to     |
      |                                               | 1000 Mbps  |
      +-----------------------------------------------+------------+

           Table 1: Throughput requirement for streaming video
                               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.

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   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 [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.  This requirement for response
   time is similar to the first two entries of Table 2 below.
   Additionally, 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.  Conclusion

   In order to operationalize a use case such as the one presented in
   this document, a network operator could dimension their network to
   provide a short and high-capacity network path from the edge compute
   resources or storage to the mobile devices running the XR
   application.  This is required to ensure a response time of 20ms at
   most and preferably between 7-15ms.  Additionally, a bandwidth of 200
   to 1000Mbps is required by such applications.  To deal with the
   characteristics of XR traffic as discussed in this document, network
   operators could deploy a managed edge cloud service that
   operationally provides dynamic placement of XR servers, mobility
   support and energy management.  Although the use case is technically
   feasible, economic viability is an important factor that must be
   considered.

6.  IANA Considerations

   This document has no IANA actions.

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7.  Security Considerations

   The security issues for the presented use case are similar to other
   streaming applications [DIST], [NIST1], [CWE], [NIST2].  This
   document itself introduces no new security issues.

8.  Acknowledgements

   Many Thanks to Spencer Dawkins, Rohit Abhishek, Jake Holland, Kiran
   Makhijani, Ali Begen, Cullen Jennings, Stephan Wenger, Eric Vyncke,
   Wesley Eddy, Paul Kyzivat, Jim Guichard, Roman Danyliw, Warren
   Kumari, and Zaheduzzaman Sarker for providing very helpful feedback,
   suggestions and comments.

9.  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.

   [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.,
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              Apicharttrisorn, K., Balasubramanian, B., Chen, J.,
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Authors' Addresses

   Renan Krishna
   United Kingdom
   Email: renan.krishna@gmail.com

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

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