MOPS R. Krishna
Internet-Draft InterDigital Europe Limited
Intended status: Informational A. Rahman
Expires: May 3, 2021 InterDigital Communications, LLC
October 30, 2020
Media Operations Use Case for an Augmented Reality Application on Edge
Computing Infrastructure
draft-krishna-mops-ar-use-case-01
Abstract
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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Internet-Draft MOPS AR Use Case October 2020
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 3
3. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 3
5. Informative References . . . . . . . . . . . . . . . . . . . 5
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 6
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. 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
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].
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2. Conventions used in this document
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
3. Use Case
We now descibe 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 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.
Offloading to the remote Cloud is not feasible for applications with
AR characteristics as the end-to-end delays must be within the order
of a few milliseconds. In order to achieve such hard timing
constraints, computationally intensive tasks can be offloaded to Edge
devices.
4. Requirements
As discussed above an AR application requires offloading of its
components to resources provided by Edge Computing. These components
perform tasks such as real-time generation and processing of high-
quality video content that are too computationally intensive for the
mobile device.
In addition, such applications require high bandwidth and low jitter
to provide a high QoE to the user. Another consequence of running
such computationally intensive applications on AR devices such as AR
glasses is the excessive heat generated by the chip-sets that are
involved in the computation [DEV_HEAT_1]. Finally, the battery on
such devices discharges quickly when running such applications if
some processing is not off-loaded to the Edge Computing.
Note that the Edge device providing the computation and storage is
itself limited in such resources compared to the Cloud. So, for
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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
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
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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
situation.
5. 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.
[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.
[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.
[HEAVY_TAIL_1]
Crovella, M. and B. Krishnamurthy, "Internet measurement:
infrastructure, traffic and applications", John Wiley and
Sons Inc., 2006.
[HEAVY_TAIL_2]
Taleb, N., "The Statistical Consequences of Fat Tails",
STEM Academic Press, 2020.
[I-D.ietf-mops-streaming-opcons]
Holland, J., Begen, A., and S. Dawkins, "Operational
Considerations for Streaming Media", draft-ietf-mops-
streaming-opcons-02 (work in progress), July 2020.
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[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[UBICOMP] Bardram, J. and A. Friday, "Ubiquitous Computing Systems",
In Ubiquitous Computing Fundamentals (pp. 37-94). CRC
Press, 2009.
Authors' Addresses
Renan Krishna
InterDigital Europe Limited
64, Great Eastern Street
London EC2A 3QR
United Kingdom
Email: renan.krishna@interdigital.com
Akbar Rahman
InterDigital Communications, LLC
1000 Sherbrooke Street West
Montreal H3A 3G4
Canada
Email: Akbar.Rahman@InterDigital.com
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