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In Network Computing Enablers for Extended Reality

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Author Marie-Jose Montpetit
Last updated 2018-10-23
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Apr 2020
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COIN                                                        M. Montpetit
Internet-Draft                                            Triangle Video
Intended status: Informational                          October 23, 2018
Expires: April 22, 2019

           In Network Computing Enablers for Extended Reality


   Augmented Reality (AR) and Virtual Reality (VR), combined as Extended
   Reality or XR, challenge networking technologies and protocols
   because they combine the features of fast information display, image
   processing, computing and forwarding.  This document presents some of
   these challenges and how adding computing in the network could
   respond to them.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on April 22, 2019.

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   Copyright (c) 2018 IETF Trust and the persons identified as the
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   This document is subject to BCP 78 and the IETF Trust's Legal
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   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
   2.  Definitions . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Extended Reality and In-Network Computing . . . . . . . . . .   4
     3.1.  XR Network Requirements . . . . . . . . . . . . . . . . .   4
     3.2.  In-Network Computing Advantages in XR . . . . . . . . . .   5
   4.  Enabling Technologies . . . . . . . . . . . . . . . . . . . .   6
     4.1.  Information Centric Networking (ICN) and Named Data
           Networking (NDN)  . . . . . . . . . . . . . . . . . . . .   7
     4.2.  Network Coding  . . . . . . . . . . . . . . . . . . . . .   7
     4.3.  Blockchains and Distributed Trust . . . . . . . . . . . .   8
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   9
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   9
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   Augmented and Virtual reality target different applications
   but they all share a number of stringent delay and bandwidth
   requirements to prevent confusing the brain whenever information
   about the virtual environment is not wholly consistent causing motion
   sickness symptoms [VRSICK].  Hence to now XR has been delivered
   mostly locally via combinations of computers and headsets with some
   cloud implementations being limited to time invariant imaging in one

   But with the emergence of the edge and the programmability of network
   elements all the way from the data center to the users the
   possibility of creating networked, multiparty/multisource and
   interacting XR comes closer to reality.  This document wants to
   review what is necessary for the current localized and cloud
   supported XR to evolve to a more distributed and edge centric
   architecture to support advanced immersive application and services.
   It assumes that network programmability will enable to tailor the
   network to the XR requirements.  This document is about requirements
   not solutions per se but will mention work that has already been done

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   towards a more networked XR including Information Centric
   architectures, Artificial Intelligence and in network coding.  The
   networked functionality should enable to supplement local XR services
   and devices while keeping the very low latency and the very high data
   rates that are required by XR.

   This document is intended as informative to both the networking and
   application research community.  It does not address a specific
   network layer or protocol but provides architecture and system level
   specifications and guidelines.  For example:

      Latency: the physical distance between the XR content cloud of AR/
      VR and users are short enough to limit the propagation delay to
      the 20 ms usually cited for XR applications [ref] mixed for
      example with IoT devices and sensors delay reduction for range of
      interest (RoI) detection.

      Applications: better coding and use of compression algorithms,
      pre-fetching and pre-caching and movement prediction.

      Network access: push some networking functions in the data plane
      into the user plane to enable the deployment of stream specific
      algorithms for congestion control and application-based load
      balancing based on machine learning and user data patterns.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  Definitions

      AR: Augmented Reality (AR) is a live direct or indirect view of a
      physical, real-world environment whose elements are augmented by
      computer-generated input such as sound, video, location or
      graphical data.  It is related to a more general concept called
      mediated reality [MEDIA], in which reality is modified (diminished
      or augmented) by computer-generated imagery.

      VR Virtual Reality (VR): uses software-generated realistic
      imaging, sounds and other sensor inputs to replicate a real or
      imaginary setting, to simulates a user's physical presence in this

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      environment and provide an immersive experience that enable the
      user to interact with objects and move within this space.

      360-degree video: 360-degree videos, also known as immersive
      videos or spherical videos, are video recordings where a view in
      every direction is recorded at the same time using an
      omnidirectional camera or a collection of cameras.  360o video is
      outside the scope of this document.

      XR: extended reality is used to address both AR and VR together.

3.  Extended Reality and In-Network Computing

   XR is an example of the Multisource Multidestination Problem that
   combines video, haptics and tactile experiences as well, in
   interactive or networked mode multiparty and social interactions.
   Thus, XR is difficult to deliver to deliver with a client-server
   strictly cloud-based solution as it requires a combination of stream
   synchronization, lows delay and delay variations as mentioned above
   as well as means to cover from losses and provide optimized caching
   in the cloud and rendering as close as possible to the user at the
   network edge.

3.1.  XR Network Requirements

   In order to deliver the XR experience, there is a need to achieve
   complete 6 degrees of freedom meaning the 3 axes for body movement
   (x,y,z) plus pitch, yaw, rotation of the head all of which must be
   fulfilled in real time again focusing on the low delay, low loss and
   low delay variation to avoid sea sickness symptoms if the image does
   not follow the movement [CABLE].  But this is not the only
   difficulty, as there is also the need to provide real-time
   interactivity for immersive sports, mobile immersive applications
   with tactile and time-sensitive data and high bandwidth for high
   resolution images.  Since XR deals with personal information and
   potentially protected content (in entertainment and gaming) XR must
   also provide a secure environment and ensure user privacy.  And of
   course, the sheer amount data needed for and generated by the XR
   applications will use recent trend analysis and mechanisms, including
   machine learning to find these trends and reduce the size of the data

   Shared and global immersive experiences require interconnected,
   distributed and federated XR nodes.  The requirements can be
   summarized as:

      - Allow joint collaboration in VR.

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      - Provide multi-view AR.

      - Add extra streams (IoT) to AR and VR experiences across

      - Provide "Social Television" experiences and global viewing and
      experience rooms.

      - Enable multistream, multidevice, multidestination applications.

      - Use new Internet Architectures at the edge for improved

      - Integrate with holography, 3D displays and image processing
      systems [CABLE].

3.2.  In-Network Computing Advantages in XR

   One aspect of the push of XR to the edge is of course to provide
   cloud-based services with much lower latency.  While this is very
   promising the question of the localization of the networking
   resources in order to provide the service becomes an essential
   component of the overall architecture.  But it is not only finding
   the best geographical location but also providing the right level of
   reliability when one or more location is not available especially for
   mission critical services in medicine or manufacturing.  And it does
   not mean only data laid distribution but also ensuring the
   availability of the right computational capabilities.  The
   optimization of the location and type of the required resources for
   the multisouce, multidestination, mutiparty, multi-input XR
   applications can use AI and ML, and advanced load balancing and
   distributed network principles.  There is a need for more research in
   such resource allocation problems at the edge to enable autonomous
   node operation and quality of experience [SOL].  These are of course
   multi-variate and heterogeneous goal optimization problems requiring
   advanced analysis with fast converging algorithms [MULTI][PACKET].
   This is essential for the federation of nodes to provide the required

   Of course, image rendering and video processing in XR leverages
   different HW capabilities combinations of CPU and GPU.  Current
   programmable network entities need to be evaluated to see if they can
   be sufficient to provide the speed required to provide real-time
   rendering and execute complex analytics: P4 for example does not
   support the floating-point operations necessary for advanced

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   Finally, dynamic network programmability could enable the use of
   joint learning algorithms across both data center, edge computers and
   goggle or glasses to allocate functionality and the creation of semi
   permanent datasets and analytics for usage trending.  In the end, the
   use of computing or networked XR will enable the allocation of
   control, forwarding and storage resources and related usage models
   when needed by the application.  This may mean re-evaluating the
   distribution of functionalities between datacenter and edge with less
   critical elements rendered in the cloud combined with a better
   understanding of the operational decomposition of the XR experience
   to allow the use of novel data structures, three-dimensional modeling
   and image processing algorithms.

   Other advantages of adding computing to networked XR include:

      - Multicast distribution and processing as well as peer to peer
      distribution in bandwidth constrained environments.

      - Evaluation of local caching and micro datacenters with local or
      cloud-based pre-rendering.

      - Trend or ML based congestion control to manage XR sessions
      quality of service.

      - Higher layer protocols optimization to reduce latency.

      - Trust, including blockchains and smart-contracts to enable
      secure community building across domains.

      - Support for nomadicity and mobility (link to mobile edge).

      - Use of 5G slicing to create independent session-driven

      - Performance optimization by tunneling, session virtualization
      and loss protection.

4.  Enabling Technologies

   This section presents some salient research that will lead to in-
   network computing becoming a major enabler of networked XR.

   NOTE: more information and added sub-sections will be added in future
   versions of the draft with the collaboration of co-authors in the
   specific research areas.

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4.1.  Information Centric Networking (ICN) and Named Data Networking

   The Named Data Networking (NDN) architecture, one architecture of
   ICN, is particularly well suited for the multisource multi-
   destination architecture of XR because it allows to create the
   content experiences based on their components names not a location or
   pointer to a location hence provides a natural functional
   decomposition.  ICN allows content delivery to evolve from single,
   context-independent streams to context-dependent Information
   components that can adapt dynamically to the changes necessary to
   maintain the immersive nature of the experience and be delivered
   efficiently.  The combination of interest messages to signal what
   content is needed combined with the data responses help to coordinate
   the different streams and multiple users (pull mechanisms).  The ICE-
   AR [ICE] project already mentions a concept of acceleration as a
   service: the exploration of the design and the usage of computation
   at the edge including the wireless edge.

   For XR, ICN also allows to develop robust and resilient networking
   while allowing application developer to continue using known
   programming model [RICE].  This is important for the XR developers
   community that come from the entertainment, gaming or other non
   network specific industries and could enable ICN and XR to coexist in
   user devices (the ultimate edge).  NDN concepts are already
   integrated to distributed video distribution with trust mechanisms
   (see section below) such as smart contracts on the blockchain to
   proof of origin and destination sent along with interest messages

4.2.  Network Coding

   Networked XR requires the synchronization of multiple streams but
   with its delay sensitivity the use of buffering schemes to achieve
   this synchronization is impractical.  At the same time the need to
   maintain high image quality means that packet losses also need to be
   limited.  Network coding has proven very useful to achieve both these
   goals in commercial streaming services like Netflix, is being added
   to protocols like QUIC and in another multi-stream service namely
   Social Television [SOCIAL] avoiding the reliance on complex
   synchronization algorithms.  The main difference between XR and
   Social Television is that the former is even more constrained in
   latency and loss budgets hence even the delay due to encoding and
   decoding operations needs to be minimized.  Hence the idea of in-
   network coding and re-encoding to adapt to dynamic network
   conditions, not just end to end, can be used to ensure on time packet
   delivery with loss recovery.  In network encoding needs the type of
   programmability that COIN provides.

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4.3.  Blockchains and Distributed Trust

   If XR is to be integrated at the edge of the network to provide the
   required delay and loss guarantees, then relying on centralized
   mechanisms for trust is non-realistic.  Traditional centralized
   mechanisms to discover and admit nodes to the network, to provide
   access right and name resolution need to be updated to be used in the
   dynamic XR environment.  Blockchain technology, with operation
   performed at the edge and in a decentralized way is fast becoming a
   major scalable means of providing trust and validate provenance in a
   large number of applications including those on the XR portfolio.
   Smart contracts (on the blockchain) supply a mechanism to provide the
   trust and validation for XR edge nodes.

   A new XR participant node is admitted after it has committed to a
   smart contract that contains the rules and mechanisms to distribute
   content via this node in a trusted and secure way.  This constitutes
   its proof of validity.  After a node is admitted, it will can then
   provisioned with the appropriate software to become fully operational
   to provide the XR experience.  Newly admitted nodes will be inserted
   in the general ledger on the blockchain enabling other nodes to
   discover them, and hence, to form a trusted network.  A name
   resolution authority can also be provided by the blockchain to manage
   and validate the origin of the content, the proof of origin, and to
   provide the ability to search such content.  The proof of origin can
   also be used to prevent some content from reaching one or more nodes
   and implement content filtering based on trusted authorities.  This
   is useful not only for content packets but also for packets capable
   of modifying the node operations.  Finally, when some content reaches
   a specific destination, it can be verified against the content rules
   of the reached node even and before it is sent to the application;
   this allows to provide a proof of delivery for the content and enable
   to generate statistics, performance metrics and enable the nodes to
   adapt to the XR requirements.

   All of the above assumes that the nodes can implement the functions
   needed by the blockchain hence once again infers that there is enough
   computing power in the nodes to perform these operations.  At this
   point both proof of concept and proof of every are limited due to the
   added overhead and the size of the blockchain.  As distributed
   blockchain and COIN continue to evolve this should continue to be a
   field of interest for the development of secure and private XR

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5.  Conclusion

   More and more applications and service are being developed and
   deployed that use or will use combinations of AR and VR, XR, along
   with extra stream from sensors and IoT devices.  And many of these
   applications require to be deployed over a network because of their
   interactive or multiparty nature.  In that context, it not uniquely
   necessary to move functionality to the network but to carefully
   evaluate which elements to locate in network nodes, where these nodes
   are and what computational support they need to support the XR
   experience.  Hence, it is believed that a great enabler of networked
   XR is the capability to co-locate programmable elements in the XR
   network node to respond to the dynamics of the services in an
   efficient, resilient and secure manner.

6.  Acknowledgements

   The author would like to thank Jeffrey He, Dirk Kutscher, Cedric
   Westphal and Weiguang Wang for their contributions to the
   presentation that lead to this draft.

7.  References

7.1.  Normative References

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

7.2.  Informative References

   [CABLE]    Hinds, A., "The Near Future of Immersive Experiences:
              Where We Are on the Journey, What Lies Ahead, and What It
              Takes to Get There", SIGCOMM 2018 Workshop on AR/VR
              arvr.html, August 2018.

   [HUITX]    "8X: ICN Based Video Distribution", 2018,

   [ICE]      Burke., J., "ICN-Enabled Secure Edge Networking with
              Augmented Reality: ICE-AR", ICE-AR Presentation at NDNCOM
              September 2018
              meeting-2018, 2018, <>.

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   [INTER]    Bastug et al., E., "Towards Interconnected Virtual
              Reality:Opportunities, Challenges and Enablers", IEEE
              Communications Magazine, Volume 55 , Issue: 6 ,
              2017, June 2017.

   [MEDIA]    "Mediated Reality",

   [MULTI]    Batalla, J., "Evolutionary Multiobjective optimization
              algorithm for multimedia delivery in critical applications
              through Content-Aware Networks", The Journal of
              Supercomputing, Volume 73, Issue 3, pp. 993-1016
              s11227-016-1731-x, March 2017.

   [PACKET]   Jeyakumar et al., V., "Millions of Little Minions: Using
              Packets for Low Latency Network Programming and
              Visibility", Proceedings of SIGCOMM 2014
              August 2018.

   [RICE]     Krol et al., M., "RICE: Remote Method Invocation in ICN",
              Proceedings of the ACM Conference on Information-Centric
              Networking 2018
              icn/2018/proceedings/icn18-final9.pdf, September 2018.

   [SOCIAL]   Montpetit, M. and M. Medard, "Social Television: Enabling
              Technologies and Architectures", Proceedings of the IEEE,
              Volume 100, pp.
              1395-1399, May 2012.

   [SOL]      Heorhiadi et al., V., "Simplifying Software-Defined
              Network Optimization Using SOL", 13th USENIX Symposium on
              Networked Systems Design and Implementation
              nsdi16-paper-heorhiadi.pdf, March 2016.

   [VRSICK]   LaViola, J., "A Discussion of Cybersickness in Virtual
              Environments", ACM SIGCHI Bulletin 32(1):47-56
    , January

Author's Address

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   Marie-Jose Montpetit
   Triangle Video
   Boston, MA