Towards User-centric Video Transmission in Next Generation Mobile Networks

Towards User-centric Video Transmission in Next
Generation Mobile Networks

Ali El Essaili
Klagenfurt, February 12, 2014

Institute for Media Technology
Prof. Dr.-Ing. Eckehard Steinbach

Technische Universität München

Outline
 Motivation and overview
 QoE-driven adaptive HTTP video delivery
 QoE-driven resource allocation for LTE uplink
 Conclusions

February 12, 2014

A. El Essaili: Towards User-centric Video Transmission in Next Generation Mobile Networks

2



Motivation



The majority of mobile data is streaming video and audio.
The lion share of the mobile traffic is TCP/IP based.

[1] http://www.sandvine.com/downloads/documents/Phenomena_1H_2012/Sandvine_Global_Internet_Phenomena_Report_1H_2012.pdf

February 12, 2014


3



Overview




DASH (Dynamic Adaptive Streaming over HTTP) is standardized for mobile multimedia
streaming (3GPP Rel-11, MPEG ISO/IEC 23009-1).
OTT DASH provides an end-to-end server-client adaptation. The client reacts to the
resources assigned by the scheduler in the operator network.
Objective: Enhance the adaptive HTTP media delivery in next generation mobile
networks.
Standard
DASH Client

DASH Server
LTE
network

Media Presentation

MPD
HTTP Request
February 12, 2014

QoE-driven
traffic management

End-to-end

Base
station
Adaptation
Bit-rate estimation
Segment switching


4



Overview
Difference to RTP-based QoE-driven optimization

Application
Server

Find the resource allocation which maximizes overall utility based
on application and channel conditions.
∑
… LTE network

Application
utility function

QoE optimizer
Optimal rate for
each user

Channel
information

Mobile
Users

Rate

1) In-network content adaptation is
costly, may react late
2) DASH provides inherent
adaptivity by encoding the same
content at multiple bit-rates

Traffic
management
(e.g., transcoding,
packet dropping)

Base
station

[1] S. Khan, Y. Peng, E. Steinbach, M. Sgroi, W. Kellerer, “Application-driven cross-layer optimization for video streaming over wireless networks,” IEEE
Communications Magazine, 2006.
[2] W. Kellerer, D. Svetoslav, E. Steinbach, M. Sgroi, S. Khan, EP1798897, granted June, 2008.

February 12, 2014


5



Outline
 Conclusions

February 12, 2014


6



QoE-driven adaptive HTTP video delivery
Proposed QoE-Proxy approach
Target DASH
Representation

Proxy
Rewrite client
requests

DASH
Server

LTE network
Rate

Utility

Media segments

QoE optimizer
Optimal rate for
each client

Channel
information
Standard DASH
Client

Rate
Resource
shaper
TCP rate
MPD
HTTP Request

Base
station

 The QoE optimizer returns the optimal rate of each user.
 The proxy rewrites the HTTP requests and forwards them to the DASH server.
 The DASH Server and the DASH clients are unaware of the proxy operation.
February 12, 2014


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QoE-Reactive scheme
Target DASH
Representation
LTE network
DASH
Server

Utility

Media segments

QoE optimizer
Optimal rate for
each client

Channel
information

Standard DASH
Client

Rate
Resource
shaper
TCP rate
MPD
HTTP Request

Base
station

 The TCP throughput of each client is shaped according to the optimal rate.
 The streaming rate is determined by the standard DASH client.
February 12, 2014


8



Utility curves

[1] L. Choi, M. Ivrlac, E. Steinbach, and J. Nossek, “Sequence-level methods for distortion-rate behavior of compressed video,” IEEE ICIP’05
[2] S. Khan, S. Duhovnikov, E. Steinbach, and W. Kellerer, “MOS-based multiuser multiapplication cross-layer optimization for mobile multimedia
communication,” Advances in Multimedia, 2007

February 12, 2014


9



Optimization schemes

Scheme

Description

QoE-Server

The server encodes the video stream at the optimal
rate (e.g., managed content).

QoE-Proxy

Proposed proxy-based approach (OTT content).

QoE-d-Proxy

Similar to the QoE-Proxy scheme. However, the
optimizer uses discrete utility representations.

QoE-Reactive

We shape the TCP throughput. The actual streaming
rate, however, is determined by the client.

Non-Opt

Standard OTT DASH streaming (Reference scheme)

February 12, 2014


10



Simulation parameters

LTE bandwidth

5 MHz

Number of PRBs

25

CQI update

Application parameters
Video codec

H.264 AVC, CIF, 30
fps

2 sec

Segment size

2 sec

Channel model

Urban macrocell

Number of clients

8

User speed

30 km/hr

Simulation time

60 sec

PSNR-MOS
mapping

Linear: {(1, 30 dB),
(4.5, 42 dB)}

Simulation runs

50

Client

Standard Microsoft
Smooth Streaming

February 12, 2014


11



Experimental results
 CDF of the mean MOS for 8 users over 50 runs
1
0.9

Mean MOS gain

0.8

QoE-Reactive

CDF

0.6

0.19

QoE-Proxy

0.7

0.34

QoE-d-Proxy

0.49

QoE-Server

0.57

0.5
0.4

Non-Opt
QoE-Reactive
QoE-Proxy
QoE-d-Proxy
QoE-Server

0.3
0.2
0.1
0

3

3.5

4

4.5

Mean MOS
February 12, 2014


12



Experimental results
 Individual performance of 8 users over 50 runs
Substantial gains for resource-demanding videos

February 12, 2014


13



Subjective tests
Settings
 SAMVIQ (Subjective Assessment of Multimedia Video Quality) method [1]
 20 test subjects after screening procedure
 2 scenarios:
□ Scenario 1: users move at a speed of 30km/h
□ Scenario 2: users move at a speed of 120 km/h
 8 users in the cell
 10 seconds of video (without starting phase)
 Differential MOS is used as subjective quality rating [2]
□ DMOS(sequence) = R(sequence) – R(hidden ref) + 100

[1] ITU-T Rec. BT.1788 Methodology for subjective assessment of video quality in multimedia applications, 2007
[2] ITU-T Rec. P.910 Subjective video quality assessment methods for multimedia applications, 2008

February 12, 2014


14



Subjective tests
User interface

February 12, 2014


15



Subjective tests
Overall results
100

mean DMOS (8 users)

90

80

70

60

50
mean DMOS rating
40
QoE-Proxy

QoE-Reactive

Non-Opt

Scenario 1: 30 Km/h

QoE-Proxy

QoE-Reactive

Non-Opt

Scenario 2: 120 Km/h

 Higher mean MOS, improvements more notable in dynamic scenario.
 Fair user experience, up to 35% MOS increase for worst-case user.
February 12, 2014


16



Video demo

QoE-Proxy

February 12, 2014

vs

QoE-Reactive


17



Outline
 Conclusions

February 12, 2014


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QoE-driven resource allocation for LTE uplink
Challenges

February 12, 2014


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Use-cases

 Use-case 1:
□ Service‐centric approach for uplink distribution of real‐time usergenerated video content.
□ Based on QoE and popularity of the video content.
 Use-case 2:
□ Joint upstream of live and time‐shifted video content under scarce
uplink resources.
□ Transmit a basic quality in real‐time and upload a refined quality
for on‐demand consumption.

February 12, 2014


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Use-cases

 Use-case 1:
□ Service‐centric approach for uplink distribution of real‐time usergenerated video content.
□ Based on QoE and popularity of the video content.
 Use-case 2:
□ Joint upstream of live and time‐shifted video content under scarce
uplink resources.
□ Transmit a basic quality in real‐time and upload a refined quality
for on‐demand consumption.

February 12, 2014


21



Proposed approach
 Motivation: Mobile networks will have to deal with a vast increase in user-generated
video content given limited resources in the wireless uplink.
 Contribution: We propose a QoE-driven approach for jointly optimizing the uplink
transmission of live and on-demand videos.

February 12, 2014


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Application model
 Utility function for video streaming is defined on the MOS scale.
 A Group of Pictures (GoP) is encoded into a set of video layers with a common
deadline (i.e., base layer (BL), enhancement layer (EL)).
 Joint optimization of video layers with playout deadline d1 (live) and cached layers
with playout deadline d2 (on-demand).

S L  set of all deadlines
February 12, 2014


23



Optimization functions
 Network-based QoE optimization that maximizes the sum of utilities of K users:
K

arg max  U k (ck )
( c1 ,..., c K )

k 1

 Distributed QoE optimization for each user k:
arg max
al , Rl ,S L ,d ,lS L ,d

Uk  

S L ,d lS L ,d

 a R
s.t.

S L ,d lS L ,d



 a  b  MOS ( R )

l

l

l

 ck

 Ll (1  al )  H k

S L ,d lS L ,d

 al tl  d , S L,d

lS L ,d

February 12, 2014

l

l

l

ck : instantaneous uplink capacity
H k : cache size
MOSl : additional QoE improvement
Ll : size, Rl : instantaneous rate
tl : time to transmit
al : 1 if scheduled, 0 otherwise
bl  {0,1} describes layer dependencies


24



Simulation results

Scheme

Description

MOS-CLO

QoE distributed and QoEdriven network optimization

LTE bandwidth

5 MHz

QoE distributed and PF

Channel model

Urban macrocell

Scheduling metric

Proportional Fair (PF)

Number of upstream
users

4: Bus, Football, Soccer,
Foreman

Video codec

H.264/SVC, CIF, 30 fps,
4 layers

Relative cache size

0.3

(Live, VoD) delay

(266 ms, 20 sec)

Simulation time, runs

100 sec, 20

Simulator

LTE OPNET 16.0

MOS-PF
4
3,75

Mean MOS

3,5
3,25
3
2,75
2,5
2,25
2
1,75
1,5
MOS-CLO
Live

MOS-CLO
VoD

MOS-PF
Live

Scheme

February 12, 2014

MOS-PF
VoD


25



Demo

February 12, 2014


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Outline
 Conclusions

February 12, 2014


27



Conclusions
 Exploit the benefits of QoE-driven traffic management:
□ Propose a proactive approach for rewriting the client requests.
□ Requires no adaptation of the media content, suitable for OTT services.
□ Main results: improved QoE, fairness, and network awareness.
 Playout buffer-aware traffic and resource management.
 Service-centric approach for uplink resource allocation:
□ Higher QoE compared to standard scheduling mechanisms in LTE.
□ Improved video quality and efficient usage of the network resources by
considering the consumers' consumption patterns.

February 12, 2014


28



Acknowledgments


Prof. Eckehard Steinbach (Institute for Media Technology, TUM)



Damien Schroeder (Institute for Media Technology, TUM)



Prof. Wolfgang Kellerer (Institute for Communication Networks, TUM)



Dr. Dirk Staehle (DoCoMo Euro-Labs)



Dr. Liang Zhou (Nanjing University of Posts and Telecommunications)

February 12, 2014


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Towards User-centric Video Transmission in Next Generation Mobile Networks

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