Internet DRAFT - draft-ietf-mops-ar-use-case
draft-ietf-mops-ar-use-case
MOPS R. Krishna
Internet-Draft InterDigital Europe Limited
Intended status: Informational A. Rahman
Expires: 14 September 2023 Ericsson
13 March 2023
Media Operations Use Case for an Extended Reality Application on Edge
Computing Infrastructure
draft-ietf-mops-ar-use-case-10
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, we discuss those
applications that run on devices having different form factors 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. We discuss the expected
behavior of XR applications which can be used to manage the traffic.
In addition, we discuss the service requirements of XR applications
to be able to run on the network.
Status of This Memo
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Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions used in this document . . . . . . . . . . . . . . 4
3. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. Processing of Scenes . . . . . . . . . . . . . . . . . . 4
3.2. Generation of Images . . . . . . . . . . . . . . . . . . 5
4. Requirements . . . . . . . . . . . . . . . . . . . . . . . . 6
5. AR Network Traffic . . . . . . . . . . . . . . . . . . . . . 8
5.1. Traffic Workload . . . . . . . . . . . . . . . . . . . . 8
5.2. Traffic Performance Metrics . . . . . . . . . . . . . . . 9
6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 11
7. Informative References . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 16
1. Introduction
Extended Reality (XR) is a term that includes Augmented Realty (AR),
Virtual Reality (VR) and Mixed Realty (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].
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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
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 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]. 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
In this document, we discuss 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. Examples of such form factors include Head Mounted Displays
(HMD) such as Optical-see through HMDs and video-see-through HMDs and
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]. In addition, network operators can
expect that the parameters that characterize the expected behavior of
XR applications are heavy-tailed. Such workloads require appropriate
resource management policies to be used on the Edge. The service
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requirements of XR applications are also challenging when compared to
the current video applications. In particular several QoE factors
such as motion sickness are unique to XR applications and must be
considered when operationalizing a network. We motivate these issues
with a use-case that we present in the following sections.
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.
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Acquisition of a model of the real world: The tracked natural
features are used to develop an annotated point cloud 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[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. Assuming distant light sources, the single point is
approximately valid for small scenes. For larger scenes, a 5D
lightfield is used which encodes seperate 2D lightfields for many 3D
positions in space [AUGMENTED].
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.
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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.
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].
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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
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]:
* 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.
* 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.
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* 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
5.1. 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 UE device has parameters such
as volume of XR data, burst time, and idle time that are heavy
tailed.
Table 1 below shows various XR applications and their associated
throughput requirements [METRICS_1]. Our use case envisages a 6DoF
video or point cloud and so 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. Self-similar traffic contains bursts at a wide
range of time scales. Multi-fractal spectrum bursts for traffic
summarizes the statistical distribution of local scaling exponents
found in a traffic trace. 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. In addition,
multi-fractal spectrum burstiness at the scale of milli-seconds could
induce jitter contributing to motion sickness. 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 cloud service could operationally provide include dynamic
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placement of XR servers, mobility support and energy management
[METRICS_6]. Providing Edge server support for the techniques being
developed at the DETNET and RAW Working Groups at the IETF could
guarantee performance of XR applications.
+===================================+=====================+
| Application | Throughput Required |
+===================================+=====================+
| Image and Workflow Downloading | 1 Mbps |
+-----------------------------------+---------------------+
| Video Conferencing | 2 Mbps |
+-----------------------------------+---------------------+
| 3D Model and Data Visualization | 2 to 20 Mbps |
+-----------------------------------+---------------------+
| Two way Telepresence | 5 to 25 Mbps |
+-----------------------------------+---------------------+
| Current-Gen 360 degree video (4K) | 10 to 50 Mbps |
+-----------------------------------+---------------------+
| Next-Gen 360 degree video (8K, | 50 to 200 Mbps |
| 90+ FPS, HDR, Stereoscopic) | |
+-----------------------------------+---------------------+
| 6DoF Video or Point Cloud | 200 to 1000 Mbps |
+-----------------------------------+---------------------+
Table 1: Throughput of some XR 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.
5.2. Traffic Performance Metrics
The performance requirements for AR/VR traffic have characteristics
that need to be considered when operationalizing a network. We now
discuss these characteristics.
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 [AR_TRAFFIC] .The following issues were identified.:
* The uploading of data from an AR 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 AR device uploads additional visual
data for processing to the remote server.
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* AR applications tend to have large bursts that are separated by
significant time gaps.
The packet loss rates in wireless links between XR devices and the
Edge server can be as high as 2% or more [WIRELESS_1].
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 expected end-to-end latencies and bandwidths.
Our use case requires an end-to-end latency of 20ms at most and
preferably between 7-15ms as discussed earlier. The required
bandwidth for our use case as discussed in section 5.2 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 | Expected | Expected | Possible |
| | End-To-End | Data | Implementations/ |
| | Latency | Latency | Examples |
+===================+==============+==========+=====================+
| AR-based remote | Less than | Greater | World's first |
| surgery with | 750 | than 30 | remote surgery |
| uncompressed 4K | microseconds | Gbps | over 5G |
| (3840x2160 | | | |
| pixels) 120 fps | | | |
| HDR 10-bit real | | | |
| time video stream | | | |
+-------------------+--------------+----------+---------------------+
| Mobile AR 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 AR | milliseconds | 100 Mbps | AR-enhanced |
| applications | | | Google Translate |
+-------------------+--------------+----------+---------------------+
Table 2: Traffic Performance Metrics of Selected XR Applications
6. Acknowledgements
Many Thanks to Spencer Dawkins, Rohit Abhishek, Jake Holland, Kiran
Makhijani ,Ali Begen and Cullen Jennings for providing very helpful
feedback suggestions and comments.
7. Informative References
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Video Streaming with Pensieve", In Proceedings of the
Conference of the ACM Special Interest Group on Data
Communication, pp. 197-210, 2017.
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[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
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(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
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[AUGMENTED]
Schmalstieg, D. S. and T.H. Hollerer, "Augmented
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[AUGMENTED_2]
Azuma, R. T., "A Survey of Augmented
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[BATT_DRAIN]
Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S.,
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[BLUR] Kan, P. and H. Kaufmann, "Physically-Based Depth of Field
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[CLOUD] Corneo, L., Eder, M., Mohan, N., Zavodovski, A., Bayhan,
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[DEV_HEAT_1]
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"Draining our Glass: An Energy and Heat characterization
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Workshop on Systems pp. 1-7, 2013.
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[DEV_HEAT_2]
Matsuhashi, K., Kanamoto, T., and A. Kurokawa, "Thermal
model and countermeasures for future smart glasses.",
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[GLB_ILLUM_1]
Kan, P. and H. Kaufmann, "Differential irradiance caching
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[HEAVY_TAIL_1]
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[HEAVY_TAIL_2]
Taleb, N., "The Statistical Consequences of Fat Tails",
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[LENS_DIST]
Fuhrmann, A. and D. Schmalstieg, "Practical calibration
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[METRICS_1]
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Wave of Killer Apps.",
https://gsacom.com/paper/augmented-virtual-reality-first-
wave-5g-killer-apps-qualcomm-abi-research/, 2017.
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[METRICS_2]
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[METRICS_3]
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[METRICS_4]
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[METRICS_6]
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Tutorials, Vol 23, No. 2, 2021.
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[OCCL_1] Breen, D.E., Whitaker, R.T., and M. Tuceryan, "Interactive
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Authors' Addresses
Renan Krishna
InterDigital Europe Limited
64, Great Eastern Street
London
EC2A 3QR
United Kingdom
Email: renan.krishna@interdigital.com
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Akbar Rahman
Ericsson
8275 route Transcanadienne
Montreal H4S 0B6
Canada
Email: rahmansakbar@yahoo.com
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