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
draft-ietf-mops-ar-use-case-03
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.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on April 28, 2022.
Copyright Notice
Copyright (c) 2021 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
Krishna & Rahman Expires April 28, 2022 [Page 1]
Internet-Draft MOPS AR Use Case October 2021
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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
Krishna & Rahman Expires April 28, 2022 [Page 2]
Internet-Draft MOPS AR Use Case October 2021
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",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
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
[AUGMENTED].
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
Krishna & Rahman Expires April 28, 2022 [Page 3]
Internet-Draft MOPS AR Use Case October 2021
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.
Krishna & Rahman Expires April 28, 2022 [Page 4]
Internet-Draft MOPS AR Use Case October 2021
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
Krishna & Rahman Expires April 28, 2022 [Page 5]
Internet-Draft MOPS AR Use Case October 2021
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
situation.
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]
Krishna & Rahman Expires April 28, 2022 [Page 6]
Internet-Draft MOPS AR Use Case October 2021
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.
[AR_TRAFFIC]
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.
[AUGMENTED]
Schmalstieg, D. and T. Hollerer, "Augmented
Reality", Addison Wesley, 2016.
[BATT_DRAIN]
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.,
2017.
[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.
Krishna & Rahman Expires April 28, 2022 [Page 7]
Internet-Draft MOPS AR Use Case October 2021
[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.
[DEV_HEAT_2]
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.
[GLB_ILLUM_1]
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.
[GLB_ILLUM_2]
Franke, T., "Delta voxel cone tracing.", In IEEE
International Symposium on Mixed and Augmented Reality
(ISMAR), pp. 39-44, 2014.
[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-07 (work in progress), September 2021.
[LENS_DIST]
Fuhrmann, A. and D. Schmalstieg, "Practical calibration
procedures for augmented reality.", In Virtual
Environments 2000, pp. 3-12. Springer, Vienna, 2000.
Krishna & Rahman Expires April 28, 2022 [Page 8]
Internet-Draft MOPS AR Use Case October 2021
[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.", https://www.roadtovr.com/oculus-
shares-5-key-ingredients-for-presence-in-virtual-reality/,
2014.
[PER_SENSE]
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.
[PHOTO_REG]
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,
<https://www.rfc-editor.org/info/rfc2119>.
Krishna & Rahman Expires April 28, 2022 [Page 9]
Internet-Draft MOPS AR Use Case October 2021
[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,
2014.
[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).",
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3453, 2019.
[VIS_INTERFERE]
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.",
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3534, 2020.
Authors' Addresses
Krishna & Rahman Expires April 28, 2022 [Page 10]
Internet-Draft MOPS AR Use Case October 2021
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
Krishna & Rahman Expires April 28, 2022 [Page 11]
