< draft-ietf-mops-ar-use-case-03.txt   draft-ietf-mops-ar-use-case-04.txt >
MOPS R. Krishna MOPS R. Krishna
Internet-Draft InterDigital Europe Limited Internet-Draft InterDigital Europe Limited
Intended status: Informational A. Rahman Intended status: Informational A. Rahman
Expires: April 28, 2022 InterDigital Communications, LLC Expires: 7 September 2022 InterDigital Communications, LLC
October 25, 2021 6 March 2022
Media Operations Use Case for an Augmented Reality Application on Edge Media Operations Use Case for an Augmented Reality Application on Edge
Computing Infrastructure Computing Infrastructure
draft-ietf-mops-ar-use-case-03 draft-ietf-mops-ar-use-case-04
Abstract Abstract
A use case describing transmission of an application on the Internet This document explores the issues involved in the use of Edge
that has several unique characteristics of Augmented Reality (AR) Computing resources to operationalize media use cases that involve
applications is presented for the consideration of the Media Extended Reality (XR) applications. In particular, we discuss those
Operations (MOPS) Working Group. One key requirement identified is applications that run on devices having different form factors and
that the Adaptive-Bit-Rate (ABR) algorithms' current usage of need Edge computing resources to mitigate the effect of problems such
policies based on heuristics and models is inadequate for AR as a need to support interactive communication requiring low latency,
applications running on the Edge Computing infrastructure. 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.
Status of This Memo Status of This Memo
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 3 2. Conventions used in this document . . . . . . . . . . . . . . 3
3. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Processing of Scenes . . . . . . . . . . . . . . . . . . 3 3.1. Processing of Scenes . . . . . . . . . . . . . . . . . . 4
3.2. Generation of Images . . . . . . . . . . . . . . . . . . 4 3.2. Generation of Images . . . . . . . . . . . . . . . . . . 5
4. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 4 4. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 5
5. AR Network Traffic and Interaction with TCP . . . . . . . . . 6 5. AR Network Traffic and Interaction with TCP . . . . . . . . . 8
6. Informative References . . . . . . . . . . . . . . . . . . . 7 6. Informative References . . . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction 1. Introduction
The MOPS draft, [I-D.ietf-mops-streaming-opcons], provides an Extended Reality (XR) is a term that includes Augmented Realty (AR),
overview of operational networking issues that pertain to Quality of Virtual Reality (VR) and Mixed Realty (MR) [XR]. AR combines the
Experience (QoE) in delivery of video and other high-bitrate media real and virtual, is interactive and is aligned to the physical world
over the Internet. However, as it does not cover the increasingly of the user [AUGMENTED_2]. On the other hand, VR places the user
large number of applications with Augmented Reality (AR) inside a virtual environment generated by a computer [AUGMENTED].MR
characteristics and their requirements on ABR algorithms, the merges the real and virtual world along a continuum that connects
discussion in this draft compliments the overview presented in that completely real environment at one end to a completely virtual
draft [I-D.ietf-mops-streaming-opcons]. environment at the other end. In this continuum, all combinations of
the real and virtual are captured [AUGMENTED].
Future AR applications will bring several requirements for the XR applications will bring several requirements for the network and
Internet and the mobile devices running these applications. AR the mobile devices running these applications. Some XR applications
applications require a real-time processing of video streams to such as AR require a real-time processing of video streams to
recognize specific objects. This is then used to overlay information recognize specific objects. This is then used to overlay information
on the video being displayed to the user. In addition some AR on the video being displayed to the user. In addition XR
applications will also require generation of new video frames to be applications such as AR and VR will also require generation of new
played to the user. Both the real-time processing of video streams video frames to be played to the user. Both the real-time processing
and the generation of overlay information are computationally of video streams and the generation of overlay information are
intensive tasks that generate heat [DEV_HEAT_1], [DEV_HEAT_2] and computationally intensive tasks that generate heat [DEV_HEAT_1],
drain battery power [BATT_DRAIN] on the AR mobile device. [DEV_HEAT_2] and drain battery power [BATT_DRAIN] on the mobile
Consequently, in order to run future applications with AR device running the XR application. Consequently, in order to run
characteristics on mobile devices, computationally intensive tasks applications with XR characteristics on mobile devices,
need to be offloaded to resources provided by Edge Computing. computationally intensive tasks need to be offloaded to resources
provided by Edge Computing.
Edge Computing is an emerging paradigm where computing resources and Edge Computing is an emerging paradigm where computing resources and
storage are made available in close network proximity at the edge of storage are made available in close network proximity at the edge of
the Internet to mobile devices and sensors [EDGE_1], [EDGE_2]. the Internet to mobile devices and sensors [EDGE_1], [EDGE_2]. These
edge computing devices use cloud technologies that enable them to
support offloaded XR applications. In particular, the edge devices
deploy cloud computing implementation techniques such as
disaggregation (breaking vertically integrated systems into
independent components with open interfaces using SDN),
virtualization (being able to run multiple independent copies of
those components such as SDN Controller apps, Virtual Network
Functions on a common hardware platform) and commoditization ( being
able to elastically scale those virtual components across commodity
hardware as the workload dictates) [EDGE_3]. Such techniques enable
XR applications requiring low-latency and high bandwidth to be
delivered by mini-clouds running on proximate edge devices
Adaptive-Bit-Rate (ABR) algorithms currently base their policy for In this document, we discuss the issues involved when edge computing
bit-rate selection on heuristics or models of the deployment resources are offered by network operators to operationalize the
environment that do not account for the environment's dynamic nature requirements of XR applications running on devices with various form
in use cases such as the one we present in this document. factors. Examples of such form factors include Head Mounted Displays
Consequently, the ABR algorithms perform sub-optimally in such (HMD) such as Optical-see through HMDs and video-see-through HMDs and
deployments [ABR_1]. Hand-held displays. Smart phones with video cameras and GPS are
another example of such devices. These devices have limited battery
capacity and dissipate heat when running. Besides as the user of
these devices moves around as they run the XR application, the
wireless latency and bandwidth available to the devices fluctuates
and the communication link itself might fail. As a result algorithms
such as those based on adaptive-bit-rate techniques that base their
policy on heuristics or models of deployment perform sub-optimally in
such dynamic environments.[ABR_1]. We motivate these issues with a
use-case that we present in the following sections.
2. Conventions used in this document 2. Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119]. document are to be interpreted as described in [RFC2119].
3. Use Case 3. Use Case
We now describe a use case that involves an application with AR We now describe a use case that involves an application with AR
skipping to change at page 6, line 12 skipping to change at page 7, line 28
transmission times.In addition, edge devices and communication links transmission times.In addition, edge devices and communication links
may fail and logical communication relationships between various may fail and logical communication relationships between various
software components change frequently as the user moves around with software components change frequently as the user moves around with
their AR device [UBICOMP]. their AR device [UBICOMP].
Thus, once the offloaded computationally intensive processing is Thus, once the offloaded computationally intensive processing is
completed on the Edge Computing, the video is streamed to the user 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 with the help of an ABR algorithm which needs to meet the following
requirements [ABR_1]: requirements [ABR_1]:
o Dynamically changing ABR parameters: The ABR algorithm must be * Dynamically changing ABR parameters: The ABR algorithm must be
able to dynamically change parameters given the heavy-tailed able to dynamically change parameters given the heavy-tailed
nature of network throughput. This, for example, may be nature of network throughput. This, for example, may be
accomplished by AI/ML processing on the Edge Computing on a per accomplished by AI/ML processing on the Edge Computing on a per
client or global basis. client or global basis.
o Handling conflicting QoE requirements: QoE goals often require * Handling conflicting QoE requirements: QoE goals often require
high bit-rates, and low frequency of buffer refills. However in high bit-rates, and low frequency of buffer refills. However in
practice, this can lead to a conflict between those goals. For practice, this can lead to a conflict between those goals. For
example, increasing the bit-rate might result in the need to fill example, increasing the bit-rate might result in the need to fill
up the buffer more frequently as the buffer capacity might be up the buffer more frequently as the buffer capacity might be
limited on the AR device. The ABR algorithm must be able to limited on the AR device. The ABR algorithm must be able to
handle this situation. handle this situation.
o Handling side effects of deciding a specific bit rate: For * Handling side effects of deciding a specific bit rate: For
example, selecting a bit rate of a particular value might result example, selecting a bit rate of a particular value might result
in the ABR algorithm not changing to a different rate so as to in the ABR algorithm not changing to a different rate so as to
ensure a non-fluctuating bit-rate and the resultant smoothness of ensure a non-fluctuating bit-rate and the resultant smoothness of
video quality . The ABR algorithm must be able to handle this video quality . The ABR algorithm must be able to handle this
situation. situation.
5. AR Network Traffic and Interaction with TCP 5. AR Network Traffic and Interaction with TCP
In addition to the requirements for ABR algorithms, there are other In addition to the requirements for ABR algorithms, there are other
operational issues that need to be considered for AR use cases such operational issues that need to be considered for AR use cases such
as the one descibed above. In a study [AR_TRAFFIC] conducted to as the one descibed above. In a study [AR_TRAFFIC] conducted to
characterize multi-user AR over cellular networks, the following characterize multi-user AR over cellular networks, the following
issues were identified: issues were identified:
o The uploading of data from an AR device to a remote server for * The uploading of data from an AR device to a remote server for
processing dominates the end-to-end latency. processing dominates the end-to-end latency.
o A lack of visual features in the grid environment can cause * A lack of visual features in the grid environment can cause
increased latencies as the AR device uploads additional visual increased latencies as the AR device uploads additional visual
data for processing to the remote server. data for processing to the remote server.
o AR applications tend to have large bursts that are separated by * AR applications tend to have large bursts that are separated by
significant time gaps. As a result, the TCP congestion window significant time gaps. As a result, the TCP congestion window
enters slow start before the large bursts of data arrive enters slow start before the large bursts of data arrive
increasing the perceived user latency. The study [AR_TRAFFIC] increasing the perceived user latency. The study [AR_TRAFFIC]
shows that segmentation latency at 4G LTE (Long Term Evolution)'s shows that segmentation latency at 4G LTE (Long Term Evolution)'s
RAN (Radio Access Network)'s RLC (Radio Link Control) layer RAN (Radio Access Network)'s RLC (Radio Link Control) layer
impacts TCP's performance during slow-start. impacts TCP's performance during slow-start.
6. Informative References 6. Informative References
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[AR_TRAFFIC] [AR_TRAFFIC]
Apicharttrisorn, K., Balasubramanian, B., Chen, J., Apicharttrisorn, K., Balasubramanian, B., Chen, J.,
Sivaraj, R., Tsai, Y., Jana, R., Krishnamurthy, S., Tran, Sivaraj, R., Tsai, Y., Jana, R., Krishnamurthy, S., Tran,
T., and Y. Zhou, "Characterization of Multi-User Augmented T., and Y. Zhou, "Characterization of Multi-User Augmented
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[AUGMENTED] [AUGMENTED]
Schmalstieg, D. and T. Hollerer, "Augmented Schmalstieg, D. S. and T.H. Hollerer, "Augmented
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Requirement Levels", BCP 14, RFC 2119, Requirement Levels", BCP 14, RFC 2119,
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[VIS_INTERFERE] [VIS_INTERFERE]
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[XR] 3GPP, "3GPP TR 26.928: Extended Reality (XR) in 5G.", [XR] 3GPP, "3GPP TR 26.928: Extended Reality (XR) in 5G.",
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Authors' Addresses Authors' Addresses
Renan Krishna Renan Krishna
InterDigital Europe Limited InterDigital Europe Limited
64, Great Eastern Street 64, Great Eastern Street
London EC2A 3QR London
EC2A 3QR
United Kingdom United Kingdom
Email: renan.krishna@interdigital.com Email: renan.krishna@interdigital.com
Akbar Rahman Akbar Rahman
InterDigital Communications, LLC InterDigital Communications, LLC
1000 Sherbrooke Street West 1000 Sherbrooke Street West
Montreal H3A 3G4 Montreal H3A 3G4
Canada Canada
Email: Akbar.Rahman@InterDigital.com Email: Akbar.Rahman@InterDigital.com
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