Network Working Group X. Zhu
Internet-Draft S. Mena
Intended status: Informational Cisco Systems
Expires: October 30, 2015 Z. Sarker
Ericsson AB
April 28, 2015
Modeling Video Traffic Sources for RMCAT Evaluations
draft-zhu-rmcat-video-traffic-source-01
Abstract
This document describes two reference video traffic source models for
evaluating RMCAT candidate algorithms. The first model statistically
characterizes the behavior of a live video encoder in response to
changing requests on target video rate. The second model is trace-
driven, and emulates the encoder output by scaling the pre-encoded
video frame sizes from a widely used video test sequence. Both
models are designed to strike a balance between simplicity,
repeatability, and authenticity in modeling the interactions between
a video traffic source and the congestion control module.
Status of This Memo
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This Internet-Draft will expire on October 30, 2015.
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Provisions Relating to IETF Documents
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Desired Behavior of Synthetic Video Traffic Model . . . . . . 3
4. Interactions Between Synthetic Video Traffic Source and
Congestion Control . . . . . . . . . . . . . . . . . . . . . 4
5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6
5.1. Time-damped response to target rate update . . . . . . . 6
5.2. Temporary burst/oscillation during transient . . . . . . 6
5.3. Output rate fluctuation at steady state . . . . . . . . . 7
5.4. Rate range limit imposed by video content . . . . . . . . 7
6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 7
6.1. Choosing the video sequence and generating the traces . . 8
6.2. Using the traces in the syntethic codec . . . . . . . . . 9
6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 9
6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 11
6.3. Varying frame rate and resolution . . . . . . . . . . . . 11
7. Combining The Two Models . . . . . . . . . . . . . . . . . . 12
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1. Normative References . . . . . . . . . . . . . . . . . . 12
9.2. Informative References . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
When evaluating candidate congestion control algorithms designed for
real-time interactive media, it is important to account for the
characteristics of traffic patterns generated from a live video
encoder. Unlike synthetic traffic sources that can conform perfectly
to the rate changing requests from the congestion control module, a
live video encoder can be sluggish in reacting to such changes.
Output rate of a live video encoder also typically deviates from the
target rate due to uncertainties in the encoder rate control process.
Consequently, end-to-end delay and loss performance of a real-time
media flow can be further impacted by rate variations introduced by
the live encoder.
On the other hand, evaluation results of a candidate RMCAT algorithm
should mostly reflect performance of the congestion control module,
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and somewhat decouple from pecularities of any specific video codec.
It is also desirable that the evaluation tests are repeatable, and be
easily duplicated across different candidate algorithms.
One way to strike a balance between the above considerations is to
evaluate RMCAT algorithms using a synthetic video traffic source
model that captures key characteristics of the behavior of a live
video encoder. To this purpose, this draft presents two reference
models. The first is based on statistical modelling; the second is
trace-driven. The draft also discusses the pros and cons of each
approach, as well as the possibility to combine both.
2. Terminology
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 RFC2119 [RFC2119].
The terminology defined in RTP [RFC3550], RTP Profile for Audio and
Video Conferences with Minimal Control [RFC3551], RTCP Extended
Report (XR) [RFC3611], Extended RTP Profile for RTCP-based Feedback
(RTP/AVPF) [RFC4585], Support for Reduced-Size RTCP [RFC5506], and
RTP Circuit Breaker Algorithm [I-D.ietf-avtcore-rtp-circuit-breakers]
apply.
3. Desired Behavior of Synthetic Video Traffic Model
A live video encoder employs encoder rate control to meet a target
rate by varying its encoding parameters, such as quantization step
size, frame rate, and picture resolution, based on its estimate of
the video content (e.g., motion and scene complexity). In practice,
however, several factors prevent the output video rate from perfectly
conforming to the input target rate.
Due to uncertainties in the captured video scene, the output rate
typically deviates from the specified target. In the presence of a
significant change in target rate, it sometimes takes several frames
before the encoder output rate converges to the new target. Finally,
while most of the frames in a live session are encoded in predictive
mode, the encoder can occasionally generate a large intra-coded frame
(or a frame partially containing intra-coded blocks) in an attempt to
recover from losses or re-sync with the receiver, or during the
transient period of responding to target rate changes.
Hence, a synthetic video source should have -
o ability to change bitrate. This includes ability to change the
framerate and/or the resolution, skip frames when required.
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o ability to fluctuate around the target bitrate set by the
congestion control module.
o ability to add delay in convergence to the target bitrate.
o ability to produce Intra-coded or repair frames on demand.
While there exists many different approaches in developing a
synthetic video traffic model, it is desirable that the outcome
follows a few common characteristics, as outlined below.
* Low Computational Complexity: The model should be computationally
lightweight, otherwise it defeats the whole purpose of serving
as a substitute for a live video encoder.
* Temporal Pattern Similarity: The individual traffic trace
instances generated by the model should mimic the temporal
pattern of those from a real video encoder.
* Statistical Accuracy: The synthetic traffic should match the
outcome of the real video encoder in terms of statistical
characteristics, such as the mean, variance, peak, and
autocorrelation coefficients of the bitrate. It is also
important that the statistical resemblance should hold across
different time scales, ranging from tens of milliseconds to
sub-seconds.
* Wide Range of Coverage: The model should be easily configurable
to cover a wide range of codec behaviors (e.g., with either
fast or slow reaction time in live encoder rate control) and
video content variations (e.g, ranging from high-motion to low-
motion).
These distinct behavior features can be characterized via simple
statistical models, or a trace-driven approach. In the next three
sections, we present an example of each.
4. Interactions Between Synthetic Video Traffic Source and Congestion
Control
Figure 1 illustrates how the synthetic video traffic source interacts
with the congestion control module and media packet transport module
at the sender. Both reference models, as described later in
Section 5 and Section 6, follow the same set of interactions.
We model the synthetic video encoder to take in raw video frames
captured by the camera along with a set of requests from the
congestion control module. It then dynamically generates a sequence
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of encoded video frames with varying size and interval. These
encoded frames are segmented and packetized into RTP packets by the
RTP stack, and encapsulated over UDP/IP before they are transmitted
to the network interface. Upon the receipt of an updated RTCP report
from the receiver, the congestion control module may further revise
its request to the synthetic video encoder, which in turn updates the
size and interval of encoded video frames at its output.
In our model, the key notion of "congestion control requests" ---
marked as (a) in the figure --- comprises several options:
o Target rate R_v(t): requested at time t from the congestion
control module to the encoder. Depending on the congestion
control algorithm in use, the update requests can either be
periodic (e.g., once per 1 second), or on-demand (e.g., only when
drastic bandwidth change over the network is observed).
o Target frame rate FPS(t): the instantaneous frame rate measured in
frames-per-second at time t. This depends on the native camera
capture frame rate as well as the target/preferred frame rate
configured by the application or user.
o Instant frame skipping: the request from the congestion control
module to skip the encoding of one or several captured video
frames, typically when a drastic decrease in available network
bandwidth is detected.
o On-demand generation of intra (I) frame: the request to encode
another I frame to avoid further error propagation at the
receiver, if severe packet losses are observed. Strictly
speaking, this request should come from the error control module,
not the congestion control module in the sender.
Optionally, the syntethic video encoder can inform the congestion
control module of the dynamic range of its output rate for the
current video contents: [R_min, R_max]. Here, R_min and R_max are
meant to capture the dynamic rate range the encoder is capable of
outputting. This typically depends on the video content complexity
and/or display type (e.g., higher R_max for video contents with
higher motion complexity, or for displays of higher resolution).
Therefore, these values will not change with R_v, but may change over
time if the content is changing.
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--------------- CC requests ---------------
raw video | | (a) | | RTCP
frames | Synthetic | <------------- | RMCAT | report
-------> | Video | | Congestion | <-------
| Encoder | -------------> | Control |
| | rate range | |
--------------- [R_min, R_max] ---------------
|
encoded |
video |
frames |
\ | /
-------------------------------------------------
| RTP stack | ------->
------------------------------------------------- RTP
packets
Figure 1: Interaction between synthetic video encoder, congestion
control, and packet transport module.
5. A Statistical Reference Model
In this section, we describe one simple statistical model of the live
video encoder traffic source. A more complete survey of popular
methods can be found in [Tanwir2013].
5.1. Time-damped response to target rate update
While the congestion control module can update its target rate
request R_v(t) at any time, our model dictates that the encoder will
only react to such changes after tau_v seconds from a previous rate
transition. In other words, when the encoder has reacted to a rate
change request at time t, it will simply ignore all subsequent rate
change requests until time t+tau_v.
5.2. Temporary burst/oscillation during transient
The output rate R_o during the period [t, t+tau_v] is considered to
be in transient. Based on observations from video encoder output
data, we model the transient behavior of an encoder upon reacting to
a new target rate request in the form of largely varying output
sizes. It is assumed that the overall average output rate R_o during
this period matches the target rate R_v. Consequently, the
occasional burst of large frames are followed by smaller-than average
encoded frames.
This temporary burst is characterized by two parameters:
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o burst duration K_d: number frames in the burst event; and
o burst size K_r: ratio of a burst frame and average frame size at
steady state.
It can be noted that these burst parameters can also be used to mimic
the insersion of a large on-demand I frame in the presence of severe
packet losses. The values of K_d and K_r are fitted to reflect the
typical ratio between I and P frames for a given video content.
5.3. Output rate fluctuation at steady state
We model output rate R_o as randomly fluctuating around the target
rate R_v after convergence. There are two variants in modeling the
random fluctuation R_e = R_o - R_v:
o As normal distribution: with a mean of zero and a standard
deviation SIGMA specified in terms of percentage of the target
rate. A typical value of SIGMA is 10 percent of target rate.
o As uniform distribution bounded between -DELTA and DELTA. A
typical value of DELTA is 10 percent of target rate.
The distribution type (normal or uniform) and model parameters (SIGMA
or DELTA) can be learned from data samples gathered from a live
encoder output.
5.4. Rate range limit imposed by video content
The output rate R_o is further clipped within the dynamic range
[R_min, R_max], which in reality are dictated by scene and motion
complexity of the captured video content. In our model, these
parameters are specified by the user.
6. A Trace-Driven Model
We now present the second approach to model a video traffic source.
This approach is based on running an actual live video encoder
offline on a set of chosen raw video sequences and using the
encoder's output traces for constructing a synthetic live encoder.
With this approach, the recorded video traces naturally exhibit
temporal fluctuations around a given target rate request R_v(t) from
the congestion control module.
The following list summarizes this approach's main steps:
1) Choose one or more representative raw video sequences.
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2) Using an actual live video encoder, encode the sequences at
various bitrates. Keep just the sequences of frame sizes for each
bitrate.
3) Construct a data structure that contains the output of the
previous step. The data structure should allow for easy bitrate
lookup.
4) Upon a target bitrate request R_v(t) from the controller, look up
the closest bitrates among those previously stored. Use the frame
size sequences stored for those bitrates to approximate the frame
sizes to output.
5) The output of the synthetic encoder contains "encoded" frames with
random contents but with realistic sizes.
Section 6.1 explains steps 1), 2), and 3), Section 6.2 elaborates on
steps 4) and 5). Finally, Section 6.3 briefly discusses the
possibility to extend the model for supporting variable frame rate
and/or variable frame resolution.
6.1. Choosing the video sequence and generating the traces
The first step we need to perform is a careful choice of a set of
video sequences that are representative of the use cases we want to
model. Our use case here is video conferencing, so we must choose a
low-motion sequence that resembles a "talking head", for instance a
news broadcast or a video capture of an actual conference call.
The length of the chosen video sequence is a tradeoff. If it is too
long, it will be difficult to manage the data structures containing
the traces we will produce in the next steps. If it is too short,
there will be an obvious periodic pattern in the output frame sizes,
leading to biased results when evaluating congestion controller
performance. In our experience, a one-minute-long sequence is a fair
tradeoff.
Once we have chosen the raw video sequence, denoted S, we use a live
encoder, e.g. [H264] or [HEVC] to produce a set of encoded
sequences. As discussed in Section 3, a live encoder's output
bitrate can be tuned by varying three input parameters, namely,
quantization step size, frame rate, and picture resolution. In order
to simplify the choice of these parameters for a given target rate,
we assume a fixed frame rate (e.g. 25 fps) and a fixed resolution
(e.g., 480p). See section 6.3 for a discussion on how to relax these
assumptions.
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Following these simplifications, we run the chosen encoder by setting
a constant target bitrate at the beginning, then letting the encoder
vary the quantization step size internally while encoding the input
video sequence. Besides, we assume that the first frame is encoded
as an I-frame and the rest are P-frames. We further assume that the
encoder algorithm does not use knowledge of frames in the future so
as to encode a given frame.
We define R_min and R_max as the minimum and maximum bitrate at which
the synthetic codec is to operate. We divide the bitrate range
between R_min and R_max in n_s + 1 bitrate steps of length l = (R_max
- R_min) / n_s. We then use the following simple algorithm to encode
the raw video sequence.
r = R_min
while r <= R_max do
Traces[r] = encode_sequence(S, r, e)
r = r + l
where function encode_sequence takes as parameters, respectively, a
raw video sequence, a constant target rate, and an encoder algorithm;
it returns a vector with the sizes of frames in the order they were
encoded. The output vector is stored in a map structure called
Traces, whose keys are bitrates and values are frame size vectors.
The choice of a value for n_s is important, as it determines the
number of frame size vectors stored in map Traces. The minimum value
one can choose for n_s is 1, and its maximum value depends on the
amount of memory available for holding map Traces. A reasonable
value for n_s is one that makes the steps' length l = 200 kbps. We
will further discuss step length l in the next section.
6.2. Using the traces in the syntethic codec
The main idea behind the trace-based synthetic codec is that it
mimics a real live codec's rate adaptation when the congestion
controller updates the target rate R_v(t). It does so by switching
to a different frame size vector stored in the map Traces when
needed.
6.2.1. Main algorithm
We maintain two variables r_current and t_current:
* r_current points to one of the keys of the map Traces. Upon a
change in the value of R_v(t), typically because the congestion
controller detects that the network conditions have changed,
r_current is updated to the greatest key in Traces that is less than
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or equal to the new value of R_v(t). For the moment, we assume the
value of R_v(t) to be clipped in the range [R_min, R_max].
r_current = r
such that
( r in keys(Traces) and
r <= R_v(t) and
(not(exists) r' in keys(Traces) such that r < r' <= R_v(t)) )
* t_current is an index to the frame size vector stored in
Traces[r_current]. It is updated every time a new frame is due. We
assume all vectors stored in Traces to have the same size, denoted
size_traces. The following equation governs the update of t_current:
if t_current < SkipFrames then
t_current = t_current + 1
else
t_current = ((t_current+1-SkipFrames) % (size_traces- SkipFrames))
+ SkipFrames
where operator % denotes modulo, and SkipFrames is a predefined
constant that denotes the number of frames to be skipped at the
beginning of frame size vectors after t_current has wrapped around.
The point of constant SkipFrames is avoiding the effect of
periodically sending a (big) I-frame followed by several smaller-
than-normal P-frames. We typically set SkipFrames to 20, although it
could be set to 0 if we are interested in studying the effect of
sending I-frames periodically.
We initialize r_current to R_min, and t_current to 0.
When a new frame is due, we need to calculate its size. There are
three cases:
a) R_min <= R_v(t) < Rmax: In this case we use linear interpolation
of the frame sizes appearing in Traces[r_current] and
Traces[r_current + l]. The interpolation is done as follows:
size_lo = Traces[r_current][t_current]
size_hi = Traces[r_current + l][t_current]
distance_lo = ( R_v(t) - r_current ) / l
framesize = size_hi * distance_lo + size_lo * (1 - distance_lo)
b) R_v(t) < R_min: In this case, we scale the trace sequence with
the lowest bitrate, in the following way:
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factor = R_v(t) / R_min
framesize = max(1, factor * Traces[R_min][t_current])
c) R_v(t) >= R_max: We also use scaling for this case. We use the
trace sequence with the greatest bitrate:
factor = R_v(t) / R_max
framesize = factor * Traces[R_max][t_current]
In case b), we set the minimum to 1 byte, since the value of factor
can be arbitrarily close to 0.
6.2.2. Notes to the main algorithm
* Reacting to changes in target bitrate. Similarly to the
statistical model presented in Section 5, the trace-based synthetic
codec has a time bound, tau_v, to reacting to target bitrate changes.
If the codec has reacted to an update in R_v(t) at time t, it will
delay any further update to R_v(t) to time t + tau_v. Note that, in
any case, the value of tau_v cannot be chosen shorter than the time
between frames, i.e. the inverse of the frame rate.
* I-frames on demand. The synthetic codec could be extended to
simulate the sending of I-frames on demand, e.g., as a reaction to
losses. To implement this extension, the codec's API is augmented
with a new function to request a new I-frame. Upon calling such
function, t_current is reset to 0.
* Variable length l of steps defined between R_min and R_max. In the
main algorithm's description, the step length l is fixed. However,
if the range [R_min, R_max] is very wide, it is also possible to
define a set of steps with a non-constant length. The idea behind
this modification is that the difference between 400 kbps and 600
kbps as bitrate is much more important than the difference between
4400 kbps and 4600 kbps. For example, one could define steps of
length 200 Kbps under 1 Mbps, then length 300 kbps between 1 Mbps and
2 Mbps, 400 kbps between 2 Mbps and 3 Mbps, and so on.
6.3. Varying frame rate and resolution
The trace-based synthetic codec model explained in this section is
relatively simple because we have fixed the frame rate and the frame
resolution. The model could be extended to have variable frame rate,
variable frame resolution, or both.
When the video quality for a given bitrate is low, one can decrease
the frame rate (if the video sequence is currently low motion) or the
frame resolution in order to attempt an improvement in the quality-
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of-experince (QoE). On the other hand, if the bitrate increases to a
point where there is no longer a perceptible improvement in the QoE,
then we might afford to increase the frame resolution or the frame
rate (useful if the video is currently high motion).
Many techniques have been proposed to choose over time the best
values for these two parameters, together with the quatization step
size, in order to maximize the quality of live video codecs
[Ozer2011], [Hu2010]. In the future, we will consider extending the
trace-based codec to be able to use variable frame rate and/or
resolution.
From the perspective of congestion control performance, varying the
frame resolution will not impact the outcome of a synthetic video
codec: the resulting encoded video frames bear the same data size
regardless of resolution choice. On the other hand, different
choices of frame rates lead to different levels of burstiness in the
encoded video traffic trace: e.g., many small packets at a high frame
rate vs. sparsely spaced large packets at a low frame rate. Such
difference in traffic profiles may affect the performance of
congestion control differently, especially when outgoing packets are
not paced at the transport module. We leave the investigation of
varying frame rate to future work.
7. Combining The Two Models
This section discusses the pros and cons of the two reference models,
as well as how one may combine them for evaluation of RMCAT
candidates.
[TODO: Add more details to this place-holder section.]
8. IANA Considerations
There are no IANA impacts in this memo.
9. References
9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[RFC3550] Schulzrinne, H., Casner, S., Frederick, R., and V.
Jacobson, "RTP: A Transport Protocol for Real-Time
Applications", STD 64, RFC 3550, July 2003.
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[RFC3551] Schulzrinne, H. and S. Casner, "RTP Profile for Audio and
Video Conferences with Minimal Control", STD 65, RFC 3551,
July 2003.
[RFC3611] Friedman, T., Caceres, R., and A. Clark, "RTP Control
Protocol Extended Reports (RTCP XR)", RFC 3611, November
2003.
[RFC4585] Ott, J., Wenger, S., Sato, N., Burmeister, C., and J. Rey,
"Extended RTP Profile for Real-time Transport Control
Protocol (RTCP)-Based Feedback (RTP/AVPF)", RFC 4585, July
2006.
[RFC5506] Johansson, I. and M. Westerlund, "Support for Reduced-Size
Real-Time Transport Control Protocol (RTCP): Opportunities
and Consequences", RFC 5506, April 2009.
[I-D.ietf-avtcore-rtp-circuit-breakers]
Perkins, C. and V. Singh, "Multimedia Congestion Control:
Circuit Breakers for Unicast RTP Sessions", draft-ietf-
avtcore-rtp-circuit-breakers-05 (work in progress),
February 2014.
[I-D.ietf-rmcat-eval-criteria]
Singh, V. and J. Ott, "Evaluating Congestion Control for
Interactive Real-time Media", draft-ietf-rmcat-eval-
criteria-01 (work in progress), March 2014.
[I-D.ietf-rmcat-cc-requirements]
Jesup, R., "Congestion Control Requirements For RMCAT",
draft-ietf-rmcat-cc-requirements-04 (work in progress),
April 2014.
[H264] ITU-T Recommendation H.264, "Advanced video coding for
generic audiovisual services",
<http://www.itu.int/rec/T-REC-H.264-201304-I>.
[HEVC] ITU-T Recommendation H.265, "High efficiency video
coding", .
9.2. Informative References
[I-D.ietf-rtcweb-use-cases-and-requirements]
Holmberg, C., Hakansson, S., and G. Eriksson, "Web Real-
Time Communication Use-cases and Requirements", draft-
ietf-rtcweb-use-cases-and-requirements-14 (work in
progress), February 2014.
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[RFC5033] Floyd, S. and M. Allman, "Specifying New Congestion
Control Algorithms", BCP 133, RFC 5033, August 2007.
[Hu2010] Hu, H., Ma, Z., and Y. Wang, "Optimization of Spatial,
Temporal and Amplitude Resolution for Rate-Constrained
Video Coding and Scalable Video Adaptation", inproceedings
in Proc. 19th IEEE International Conference on Image
Processing (ICIP'12), September 2012.
[Ozer2011]
Ozer, J., "Video Compression for Flash, Apple Devices and
HTML5", ISBN ISBN-13:978-0976259503, 2011.
[Tanwir2013]
Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic
Models", journal IEEE Communications Surveys and
Tutorials, vol. 15, no. 5, pp. 1778-1802., October 2013.
Authors' Addresses
Xiaoqing Zhu
Cisco Systems
12515 Research Blvd., Building 4
Austin, TX 78759
USA
Email: xiaoqzhu@cisco.com
Sergio Mena de la Cruz
Cisco Systems
EPFL, Quartier de l'Innovation, Batiment E
Ecublens, Vaud 1015
Switzerland
Email: semena@cisco.com
Zaheduzzaman Sarker
Ericsson AB
Luleae, SE 977 53
Sweden
Phone: +46 10 717 37 43
Email: zaheduzzaman.sarker@ericsson.com
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