ict-adamantium wp4 pqos models and adaptation mechanisms

ICT-ADAMANTIUM

WP4PQoS models and adaptation

mechanisms

ICT-ADAMANTIUM

OutlineOverviewObjectives, Progress and

AchievementsTasks

T4.1T4.2T4.3T4.4

Conclusions

ICT-ADAMANTIUM

WP4: PQoS Models and Adaptation Mechanisms

Leader: UOPDuration: M1-M24 Status: CompletedMMs Allocation Planned vs. Spent

ICT-ADAMANTIUM

Objectives and ProgressObjective Progress Report

Research and development of Voice Quality context-aware PQoS models Objective Fulfilled D4.1F

Research and development of Video Quality context-aware PQoS models Objective Fulfilled D4.1F

Research the subjective temporal tolerance of the end-user when she/he experiences degraded PQoS and subjective tests of voice/video models.

Objective Fulfilled D4.1F

Mapping NQoS parameters (including access and core networks) to voice and video PQoS for VoIP and IPTV applications

Objective Fulfilled D4.2F

Research and development of dynamic service adaptation mechanisms for optimizing the delivered PQoS level. Objective Fulfilled D4.3F

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WP4: Innovation and Achievements

Developed and Subjectively evaluated novel voice/video PQoS models for VoIP and IPTV applications.

Developed simulation platforms of the overall ADAMANTIUM concept in OPNET and NS2.

Developed a Network QoS statistics to PQoS level mapping model for video.

Developed dynamic PQoS-driven adaptation algorithms for PQoS optimisation for VoIP and IPTV applications

ICT-ADAMANTIUM

Task 4.1:

Voice and Video Quality PQoS Models

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...),,,,,( FRSBRcodecjitterdelaylossfMOS

Task 4.1: Voice and Video Quality PQoS Models (1/12)

Voice/Video Modelling and Adaptation Conceptual Diagram

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Video Quality Modelling


Temporal Feature

Extraction

Spatial Feature

Extraction

Content Type

Estimation

PQoS

Model

CT, SBR, FR, …

Loss, Delay,

Jitter

Network

MOS

RNCSGSNGGSN

Video Modelling

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Voi

ce M

odel

– U

oP

ed IIR 2.93

0 if 1)(

0 if 0)(

)3.177()3.177(11.0024.0

xxH

xxHwhere

dHddId

cbpaIe )1ln(

100 5.4

1000 107)100)(60(035.01

0 16

RforMOS

RforRRRRMOS

RforMOS

ICT-ADAMANTIUM


Vid

eo M

od

el -

Uo

PSimulation set up in Opnet

H.264 over UMTS

Analysis of Results st, sd and rd filesPSNR, MOS

Video Encoding

Original YUV videoRTP trace

.264 st file

SBR, FR

BLER, MBL

Simulation Methodology

ICT-ADAMANTIUM


10 15

0

0.5

1

Frame rate (fps)

Degre

e o

f m

em

bers

hip

in1mf1 in1mf2

0.2 0.4 0.6 0.8

0

0.5

1

Content type

Degre

e o

f m

em

bers

hip

in2mf1 in2mf2

50 100150200250

0

0.5

1

Sender bitrate (kbps)

Degre

e o

f m

em

bers

hip

in3mf1 in3mf2

1 1.5 2 2.5

0

0.5

1

Mean burst length

Degre

e o

f m

em

bers

hip

in4mf1 in4mf2

0.05 0.1 0.15

0

0.5

1

Block error rate

Degre

e o

f m

em

bers

hip

in5mf1 in5mf2

1 1.5 2 2.5 3 3.5 4 4.50.5

1

1.5

2

2.5

3

3.5

4

4.5

5

MOS-measured

MO

S-p

redic

ted

Vid

eo M

od

el -

Uo

Po ANFIS based Model over UMTS networks

R2 = 87.17%RMSE = 0.2812

ICT-ADAMANTIUM


α β γ δ ε ξ η μ

4.2694 -1.4826 x 10-9

0.0656 -0.9559 -0.0261 -2.4767 -5.3168 0.3327

R2 83.52% RMSE

0.2778

Vid

eo M

od

el -

Uo

P o Regression based Model

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Vid

eo M

odel

- D

EM

Test Signal Logarithmic Function R2 factorMobile 0.1295ln(x)+0.1274 0.9759Imax 0.0563ln(x)+0.6411 0.9514M.I. 3 0.0668ln(x)+0.5747 0.9191

Da Vinci Code 0.0474ln(x)+0.6974 0.8833Warren 0.0738ln(x)+0.5210 0.9528Nasa 0.0950ln(x)+0.3892 0.9595

BBC – Africa 0.1098ln(x)+0.2702 0.9875Superman 0.0282ln(x)+0.8167 0.8859

1 2ln( )SSIMPQoS C BitRate C

where C1 and C2 are constants strongly related to the spatial and temporal activity level of the content.

ICT-ADAMANTIUM


Vid

eo M

odel

- q

PS

NR PQoS tool developed and used in

R&S protocol analyser to provide an estimate of the PSNR of H.264-encoded video.

The algorithm is based on the statistical analysis of encoded transformation coefficients.

Provide a non-reference analysis of the image quality of compressed video.

ICT-ADAMANTIUM


Comparison of Voice and Video Models

Model Content Service Codec Usage Resolution

Voice -- E-Model Voice VoIP PCM, G.723.1 and GSM

Terminal

Voice – UoP Voice VoIP AMR Terminal

Video – UoP Video VoIP/IPTV H.264 Terminal, Access QCIF

Video – DEM Video IPTV H.264 Core Network/Server

CIF

Video – qPSNR Video IPTV H.264 Terminal CIF

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Aud

iovi

sual

Mod

ellin

g

(Sim

ulat

ion

Set

up)


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The proposed low-complexity metric is based on PER and FPS

Aud

iovi

sual

Mod

el

22

10

)PER1(

FPS β β MOSAV

Coefficient Value

β0 2.284

β1 0.089

β2 1.537

R2 84.9%

RMSE 0.325

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Summary

Developed Voice PQoS Prediction Models Developed Video PQoS Prediction Models Developed Audiovisual PQoS Prediction Model Voice/video PQoS models have been used for quality

monitoring and PQoS-driven adaptation mechanisms in the ADAMANTIUM Demonstrator for VoIP and IPTV applications.

Contributed to D4.1


ICT-ADAMANTIUM

Task 4.2: Subjective aspects, content-awareness and

subjective tests

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PC-based (UOP) and Handset-based (EHU)video subjective tests

Task 4.2: Subjective aspects, content-awareness and subjective tests (1/8)

Subjective tests according to ITU-T P.910 for video

Used Absolute Category Rating (ACR) Created 6 websites for video

subjective tests 20 participants – over 3 days 90 test samples – 60 used for training

and 30 for validation

Video sequences FR (fps) SBR (kbps) BLER(%) MBL

Akiyo, Foreman, Stefan

10

48, 88,128

1, 20, 30, 40 1, 1.75, 2.5Suzie, Carphone,

Football90, 130

PC-based tests

Sample conditions

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Mobile device based tests

6 sets of video. Different sequences, 15 per test. Different transmission conditions. Videos presented randomly. 20 participants. MOS scale used for evaluation

1 bad

2 poor

3 fair

4 good

5 excellent

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Encoding Quality: Impact of Display


0 50 100 1501

1.5

2

2.5

3

3.5

4

4.5

5

SBR (kbps)

MO

S

0 50 100 1501

1.5

2

2.5

3

3.5

4

4.5

5

SBR (kbps)

MO

S

0 50 100 1501

1.5

2

2.5

3

3.5

4

4.5

5

SBR (kbps)

MO

S

akiyo Handsetakiyo PCsuzie Handsetsuzie PC

foreman Handsetforeman PCcarphone Handsetcarphone PC

stefan Handsetstefan PCfootball Handsetfootball PC

bSBRaMOS )log(*

Subjectively higher quality in PC. Lack of rescale of QCIF in mobile

annoying. Difference independent of content or

encoding level.

Derived mapping of SBR to MOS

Transmission Quality: Impact of Display

Video Seq

FR-SBR-BLER MBL

Tx Packets

Rx Packets MOSPSNR

MOSsubj

Handset

MOSsubj

PC

Stefan 10-88-20 1.75 175 172 1.40 1.0625 1.82.5 175 169 1.11 1 1.8

10-128-20 1.75 214 211 1.61 1.5625 2.1

2.5 214 208 1.24 1 2.1

Suzie 10-90-30 2.5 92 89 3.17 1.0625 2.210-130-

40 1.75 106 105 4.00 2.1875 2.5

Carphone 10-90-30 1.75 232 231 4.28 3.5 3.8

10-90-30 2.5 232 231 4.28 2.3125 2.410-130-

30 1.75 261 259 4.27 1.875 2.32.5 261 260 4.23 2.25 3.1

Football10-130-

30 2.5 95 89 1.78 1.0625 2.3

Subjectively higher quality in PC. Non-Bursty errors have no significant impact on

video quality, unless a whole I-frame is affected. PSNR provides good MOS estimation for high

motion videos and over-estimates MOS for low motion videos.

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Long Videos


Video sequences FR (fps)-SBR(kbps)-BLER MBLNews 12-80-0

12-130-012-200-012-130-1512-130-2012-130-2512-200-0512-200-10

1.75

TV series 12-80-012-130-012-200-012-130-2012-130-2512-200-0512-200-10

Basketball 15-80-015-200-015-256-015-256-05

Users cannot tolerate degradation for a certain time. Beyond this threshold users will close their video as service is “unbearable”.

Encoding Quality: Impact of Spatial Resolution on Mobile Handsets

0 50 100 150 200 2501

1.5

2

2.5

3

3.5

4

4.5

5

MO

S

SBR (kbps)

Low Motion Video Sequences

QCIF-1QCIF-2SIF

0 50 100 150 200 2501.5

2

2.5

3

3.5

4

4.5

5Medium Motion Video Sequences

SBR (kbps)

MO

S

QCIF-1QCIF-2SIF

0 50 100 150 200 2500

1

2

3

4

5High Motion Video Sequences

SBR (kbps)

MO

S

QCIF-1QCIF-2SIF

PQoS for QCIF saturates – resolution limits PQoS – especially for low motion videos.

Better to use higher spatial resolution for higher send bit rate. QCIF and SIF MOS cross point

Below this point QCIF get better quality due to low bitrate. Above which SIF get better quality and spatial resolution

becomes the more relevant factor that influence PQoS for users

ICT-ADAMANTIUM


Transmission Quality

Video Seq FR-SBR-BLER MBL MOSsubj_Handset MOSsubj_PC IPLR

HM1 - Stefan 10-88-20 1.75 1,0625 1,8 2,86%

2.5 1 1,8 3,43%

10-128-20 1.75 1,5625 2,1 2,34%

2.5 1 2,1 3,27%

LM2 - Suzie 10-90-30 2.5 1,0625 2,2 3,26%

10-130-40 1.75 2,1875 2,5 1,89%

MM2 - Carphone 10-90-30 2.5 2,3125 2,4 0,86%

10-130-30 1.75 1,875 2,3 0,77%

2.5 2,25 3,1 0,38%

HM2 - Football 10-130-30 2.5 1,0625 2,3 6,32%

Video Seq FR-SBR-BLER MBL MOSsubj_Handset IPLR

LM 12-130-15 1.75 3,67 0,32%

12-130-20 1.75 1,88 0,77%

12-130-25 1.75 0,58 3,35%

12-200-05 1.75 2,98 0,32%

12-200-10 1.75 2,73 0,77%

MM 12-130-20 1.75 3,01 0,54%

12-130-25 1.75 1,74 2,47%

12-200-05 1.75 3,87 0,25%

12-200-10 1.75 2,47 0,59%

HM 15-256-05 1.75 3,79 0,33%

Long VideosShort Videos

Video SeqFR-SBR-

BLER IPLR_CN MOSsubj_Handset

LM 12-130-00 1% 3,25 12-130-00 3% 2,52 HM 15-200-00 2% 2,47 15-200-00 5% 1,97

Different Loss Patterns (UMTS + CN)

User tolerance to CN-like losses is much higher than the tolerance to UMTS-like losses.

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Evolution of User PerceptionLength of severe degradationsDepth of degradation

Single degradation that does not decrease from the first threshold is not severely penalized by users.

Deep scattered degradations of short duration do not represent a severe degradation of users’ perception.

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

Time(s)

SS

IM

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

Time(s)

SS

IM

0 20 40 60 80 100 120 140 1600

0.5

1

Time(s)

SS

IM

0 20 40 60 80 100 120 140 1600

0.5

1

Time(s)

SS

IM 1.5s

1.5s

3s

MOS = 4.42min score = 4

MOS = 3.25min score = 3

MOS = 3.86min score = 2.7

1s

7s

MOS = 3.74min score = 2.5

0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Time (s)

SS

IM

0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Time (s)

SS

IM

0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

Time (s)

SS

IM

10s

5s

MOS=3.34

MOS=2.31

MOS=2.46

7s

6s

4s

5s

For deep degradations, the system must react with a maximum response time of 5 seconds.

For less severe degradations, the video can be maintained by users up to 7 seconds.

ICT-ADAMANTIUM


Audio-only, Video-only and Audiovisual subjective tests

Subjective tests according to ITU-T P.800, P.910 and P.911. Used Absolute Category Rating (ACR) Used a discrete 9-level quality scale 48 participants over 3 days Carried out audio-only, video-only and audiovisual tests A total of 60 samples Subjective test results were used for audiovisual modelling.

Subjective test website

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Task 4.2: Summary Carried out PC based and mobile handset based video

quality subjective tests Carried out audio-only, video-only and audiovisual quality

subjective tests Subjectively evaluated impact of display method, video

length, spatial resolution, loss pattern etc. on PQoS quality Subjectively validated audio/video PQoS models Identified temporal tolerance threshold which can be used in

adaptation mechanisms

Contributed to D4.1

ICT-ADAMANTIUM

Task 4.3:

Mapping PQoS To DiffServ/MPLS and UMTS Traffic Classes

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Delay Quality0 – 150 ms Acceptable for most calls.

150 – 400 ms Acceptable if callers are aware of impairment

> 400 ms Not acceptable

CODEC Algorithmic DelayG.711 without PLC 0.125 msG.711 with PLC 3.875 msG7.729 (compression-based) 15 ms

Packetization Delay ~20 msec

Propagation Delay <1msec

Algorithmic Delay

Delay Requirements

Task 4.3: Mapping PQoS to DiffServ/MPLS and UMTS Traffic Classes (1/12)

VoIP Mapping Framework -- Theoretical Approach

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VoIP Mapping Framework -- Experimental Approach

0 5 10 15 20 25 301

1.5

2

2.5

3

3.5

4

4.5MOS (PESQ-LQO) vs. Packet loss rate

Packet loss rate (, %)

MO

S (

PE

SQ

-LQ

O)

AMR(12.2Kb/s)AMR(4.75Kb/s)G.729(8Kb/s)G.723.1(6.3Kb/s)iLBC(15.2Kb/s)iLBC(13.3Kb/s)

Parameters AMR (12.2Kb/s) AMR (4.75Kb/s) G.729 (8Kb/s) G.723.1 (6.3Kb/s) iLBC (15.2Kb/s) iLBC (13.3Kb/s)

a 4.1522 3.1923 3.8309 3.633 3.8551 3.8083

b 1.2190 0.8776 1.0964 1.0427 1.4981 1.3723

c 0.2508 0.2022 0.2493 0.2093 0.0799 0.1010

R2 factor 0.9971 0.9981 0.9988 0.9985 0.9996 0.9991

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IPTV Mapping Framework – Theoretical Approach

Mapping of IPTV PQoS to SBR=f(BLER, MBL)

Mapping of IPTV PQoS to SBR, FR

Mapping of IPTV PQoS to SBR=f(BLER, MBL)

Mapping of IPTV PQoS to CT=f(BLER, MBL)

Mapping of IPTV PQoS to BLER, MBL

Mapping of IPTV PQoS to FR=f(BLER, MBL)

ICT-ADAMANTIUM


0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6

0,65

0,7

0 0,02 0,04 0,06 0,08 0,1 0,12

Packet Loss Rate

Q E

xpec

ted

Su

cces

full

y D

eco

dab

le F

ram

es Packet Size 188 Bytes

Packet Size 250 Bytes



P

P PI P P

I I p I B

jCN NC C jC C N C C C

GOP GOP GOP

j 1 j 1

1- *N 1- * 1- *N 1- 1- * M - 1 * 1- *N

Q

Q

dec dec-I dec- p dec-B

total - I total - P total - B total - I total - P total - B

to

p p p + p p p

N + N + NN

( N N N ) ( N N N )

( N

tal - I total - P total - B N N )

Packet Loss Rate

Expected Decodable Frame Rate Q

MOS

Expected Decodable Frame Rate Q vs. Packet Loss Rate for various packet sizes

ICT-ADAMANTIUM

Packet Loss Rate

Expected Decodable Frame Rate

Q

MOS


Expected Decodable Frame Rate Q vs. MOS for various packet sizes

For 0.01 0.05p , MOS becomes:

Pr1.01

53.0385.8

5621 ( )

Q1 0.05

-3.9204 + 1.0315

edictedMOS

p

For 0.05 0.1p , MOS becomes:

Pr1.01

53.0385.8

5621 ( )

1 Q

edictedMOS

ICT-ADAMANTIUM

Packet Loss Rate

Expected Decodable

Frame Rate Q

MOS


ICT-ADAMANTIUM

The generic framework/architecture for joint N- and P-QoS assessment


IPTV Mapping Framework -- Experimental Approach

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The experimental signal set

Sequence no.

Source Spatial complexity Temporal dynamics

1 007 Quantum of Solace (MGM/Fox, 2008) Medium Very high

2 007 Quantum of Solace (MGM/Fox, 2008) Very high Very high

3 Marley & Me (Fox, 2009) High Medium

4 Lawrence of Arabia (Horizon, 1962) High Low

5 Lawrence of Arabia (Horizon, 1962) Low Low


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Statistical Distributions…

PQoS = 100 - 3,92p

PQoS = 100 - 3,92p

PQoS = 100 – 3.92p


ICT-ADAMANTIUM

QoS PROPERTIES and mapping OF DIFFSERV CLASSES

DiffServ Class Type of Service

EF VoIP

AF1x A/V Content

BE Other data services

Diffserv Classes Mapping to Services


For both VoIP and IPTV, upper bound packet loss for acceptable quality is around 5%.

Statistical analysis of EF class by the ADAMANTIUM core network shows that the respective one-way delay is less than 50 ms and packet loss close to zero.

For AF1x classes at the core network, the respective IPLR has been measured less than 1% for all traffic schemes.

ICT-ADAMANTIUM

QoS PROPERTIES and mapping OF UMTS classes

• The conversational traffic class is marked as Expedited Forwarding to preserve the low-latency queuing behavior.

• The rate at which EF traffic is served at a given output interface should be at least the configured rate R, independent of the offered load of the non-EF traffic to that interface.

• Streaming class is marked as Assured Forwarding. Video traffic, due to its limited burst behavior and large packet size, is more problematic to manage than conversational Expedited Forwarding voice.

• The rest Assured Forwarding classifications are exploited by the Interactive traffic class.

UMTS Traffic Class

Conversational

(Real Time)

Streaming

(Real Time)

Background

(Best Effort)

Example Applications VoIP IPTV -

Diffserv Class / Map to DSCP

Expedited Forwarding Assured Forwarding Best Effort


ICT-ADAMANTIUM


Task 4.3 Summary

Carried out theoretical and experimental research on mapping of NQoS to PQoS for VoIP and IPTV

Developed mapping of Diffserv classes to PQoS degradation Developed mapping of UMTS traffic classes to PQoS

Contributed to D4.2

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Task 4.4:

Real Time Dynamic Content Adaptation Mechanisms

ICT-ADAMANTIUM

General adaptation procedure

Dec

isio

nm

akin

g

Network awareness(TNMM, ANMM)

Service awareness(MSMM, TAM)

PQoSmodels

Networkmodels

Servicemodels

Variable ParametersC

urr

ent

sta

tus

New

sta

tus

Mo

dif

iab

le p

aram

ete

r v

alu

es

Network adaptations(TNAM, ANAM)

Service adaptations(MSAM, TAM)

Task 4.4: Real Time Dynamic Content Adaptation Mechanisms (1/14)

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Decision Making

ADAMANTIUM enhanced decision making PQoS-driven Network-and service-aware Centralized approach Combined adaptations

QoE-driven Bitrate-based Service Adaptations Validation of QoE-driven approach Source of degradations: network bottleneck (+encoding) Adaptation action: application bitrate

QoE-driven Cross-layer Multi-parameter Adaptations Sources of degradations: encoding, AN, CN Adaptation actions

Different parameters at application-layer BLER control at AN (bearer switching) IPLR control at CN (promotion of CoS)


ICT-ADAMANTIUM

QoE-driven Bitrate-based Service Adaptations VoIP

44

dMOS MOS

POOR ACCEPTABLE GOOD

Z BI SI Z

S SI Z Z

B Z SD SD

IF MOS IS Good AND IF dMOS IS Z then Ctrl IS Z.

),,( ModeinstDelayPLRfinstMOS

avgMOSMOSdMOS max

IT-2 Fuzzy Controller

CtrlModeMode ni 1

Network-aware QoE-driven

AMR mode switching


ICT-ADAMANTIUM

45

Control surface

50 VoIP sessions Competing for available

network bandwidth

Number of users served at 1Mbps

MOS Threshold

Non-Adaptive aVoIP FLC

2.0 70 110 1182.5 65 85 1083.0 62 70 1003.5 55 62 854.0 55 58 60

Users above the threshold


QoE-driven Bitrate-based Service Adaptations VoIP

ICT-ADAMANTIUM

Define delta (delta=50)

if (0 < D < 0.4) and (0<C<0.2) then SBR = SBRsame (Maintain SBR)

else if (D ≥ 0.4) and (C≥0.2) then SBR = SBR – delta (Decrease SBR)

end if

end if

46

Encoder Packetizer DecoderDe-

Packetizer

Feedback Mechanism

Reference-free QoE prediction

model

Video Sender Bitrate

Adaptor

Content Classifier

Video quality measurement (PSNR/MOS)

Feedback

Raw Video Degraded Video

Network

s

l

B

BC

PQoS-driven Video Adaptation Algorithm

MOSmMOSD maxPQoS-driven

Network-aware


QoE-driven Bitrate-based Service Adaptations Mobile Video

ICT-ADAMANTIUM

47

0 0.5 1 1.5 2 2.5 3 3.5 40

0.5

1

1.5

2

2.5

3

3.5

4

Time (seconds)

MO

S

Football-Not adaptedFootball-adapted

150 200 250 300 3501.5

2

2.5

3

3.5

4

Link Bandwidth (Kbps)

MO

S

SBR-Football @88kbpsSBR-Football @128kbpsSBR-Football @200kbpsSBR-Football adapted

Time evolution under UMTS degradations

Network-aware QoE-driven

H.264 SBR switching


QoE-driven Bitrate-based Service Adaptations Mobile Video

ICT-ADAMANTIUM

Multi-parameter Cross-Layer

Decision Making

Dec

isio

nm

akin

g

Network awareness(TNMM, ANMM)

Service awareness(MSMM, TAM)

PQoSmodels

Networkmodels

Servicemodels

Variable Parameters

Cu

rren

t st

atu

s

New

sta

tus

Mo

dif

iab

le p

aram

ete

r v

alu

es

Network adaptations(TNAM, ANAM)

Service adaptations(MSAM, TAM)


ICT-ADAMANTIUM

491

23

45

67

8

0

2

4

6

8

100

5

10

15

20

25

30

35

AN statesCN states

VoI

P c

onfig

urat

ions

Service-level decision map

Task 4.4: Multi-parameter service-level decision making (8/14)

ICT-ADAMANTIUM

50

Task 4.4: Multi-parameter cross-layer decision making (9/14)

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1BLER = 0.1%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1BLER = 0.5%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1BLER = 1%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1

EC

DF

(M

OS

) BLER = 2.5%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1BLER = 5%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1BLER = 10%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1BLER = 20%

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1

MOS value

BLER = 30%

MOS<3.5 MOS<3.1 MOS<2.5min max min max min max

BLER=0.1% 0.71% 1.52% 0% 0.70% 0% 0%BLER=0.5% 4.27% 12.61% 0.67% 2.71% 0% 0%BLER=1% 9.72% 25.83% 2.63% 6.10% 0.37% 0.89%BLER=2.5% 29.85% 53.69% 12.86% 20.92% 1.72% 4.74%BLER=5% 63.93% 80.96% 38.99% 55.22% 12.27% 22.38%BLER=10% 87.92% 97.18% 80.57% 88.65% 42.61% 62.71%BLER=20% 98.42% 99.84% 98.42% 99.34% 91.43% 94.07%BLER=30% 99.46% 100% 99.46% 100% 98.49% 99.50%

ECDF of best VoIP configurations for combined AN/CN states

POORACCEPTABLEGOOD

IT-2 Inference Rules

ICT-ADAMANTIUM

51

0 5 10 15 20 25 301

1.5

2

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3

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4

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5

BLER (%)

Exp

ect

ed

MO

S

LM, SBR=200kbpsLM, SBR=130kbpsMM, SBR=200kbpsMM, SBR=130kbpsHM, SBR=256kbpsHM, SBR=200kbpsHM, SBR=130kbps

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 51

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4

4.5

IPLR (%)

Exp

ecte

d M

OS

HM, SBR=200kbpsLM, SBR=130kbps

0 5 10 15 20 250

51

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BLER (%)

IPLR (%)

Exp

ect

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S

50 100 150 200 2501

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SBR (kbps)

Exp

ect

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MO

S

LM, SIFMM, SIFHM, SIFLM, QCIFMM, QCIFHM, QCIF


Optimization Function: Integral PQoSH.264 Baseline Profile @ Level 1.2

- CT = {LM, MM, HM}- SBR = {48-256} kbps- FR = {10-15} fps- SR = {320x240, 176x144}

D4.1

D4.1

“Interactive or Background / UL:64 DL:384 kbps / PS RAB”

RLC-level 2-state Markov loss model

D4.2

D4.1

CN model:

DiffServ/MPLS CoS

IPLR model

ICT-ADAMANTIUM

52

Multimedia session 1

Application-layer

Multimedia session 2

Parameter Allowed Values VariableCT LM=Low Motion, MM=Medium

Motion, HM=High MotionNo

SBR {80, 130, 200, 256} kbps YesSR 1 (320x240), 2 (176x128) YesDLBR {384, 128} kbps YesBLER No Restrictions IndirectCoS 1 (BE), 2 (AF22), 3(AF11), 4(EF) YesIPLR No Restrictions Indirect


Optimization system variables

ICT-ADAMANTIUM

Task 4.4: GA-based decision making (12/14)

53

Update system status

PQoS Alarm

InitialPopulation

Fitness Function

Scaling Selection

ReproductionCrossOver

Max. GenerationsStall Generations

no

Optimal Individual

yes

Select Best SBR = f {CT, DL_BR, BLER}

Promote CoS of degraded sessions

Change SBR of degraded sessions

Decrease SBR and promote CoS of

degraded sessions

Initial Individual

InitialPopulation

Update IPTV structures

Assignment Matrix

Estimate new CN conditions

(D4.2)

Compute expected MOSi

(D4.1)

Compute fitness score

Genetic Algorithm

Initial Population

Fitness Function

min, MOSMOSfreFitnessSco avg

ICT-ADAMANTIUM

Optimization Results

54

0 500 1000 15001

2

3

4

5

Background Traffic Load

Ave

rage M

OS

0 500 1000 15000

1

2

3

4

5

Background Traffic Load

Min

imum

MO

S

No AdaptationCN-driven AdaptationAN/CN-driven AdaptationADAMANTIUM Adaptation

No AdaptationCN-driven AdaptationAN/CN AdaptationADAMANTIUM Adaptation

20 mobile IPTV sessions competing for available

network bandwidth

CT SBR (kbps) BLER (%)LM 130, 200 0,10,25MM 130, 200 0,10,25HM 256 0,10HM 130,200 0,10,25

Additional Background Load (Mbps)0 0.5 1 1.5

Load (kbps) 3482 3982 4482 4982IPLR (%) 0.056

20.8132 11.7571 29.45


0 2 4 6 8 10 12 14 16 18 201

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5

IPTV Session

MO

S

Background Traffic = 0 Mbps

0 2 4 6 8 10 12 14 16 18 201

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5

IPTV Session

MO

S


0 2 4 6 8 10 12 14 16 18 201

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IPTV Session

MO

S


0 2 4 6 8 10 12 14 16 18 201

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5

IPTV Session

MO

S






ICT-ADAMANTIUM

Carried out research and developed Service-level adaptation mechanisms for VoIP and IPTV

Developed per-segment network-aware cross layer adaptation mechanism

Carried out research and developed decision support for AEM Adaptation rules for AEM Intelligent AEM based on Fuzzy Controller and Genetic

Algorithm

Contributed to D4.3


Task 4.4 Summary

ICT-ADAMANTIUM

WP4 Conclusions

Developed voice/video PQoS models. Developed PQoS-driven voice/video adaptation

mechanisms both in simulation and in test-bed Developed full ADAMANTIUM simulation

environment in Opnet and NS2 Conducted extensive subjective tests and models

evaluations. Developed a framework for mapping of Network QoS

statistics to PQoS level for VoIP and IPTV

ICT-ADAMANTIUM

WP 4PQoS Models and Adaptation Mechanisms

Planned Tasks [M25 – M30]

ICT-ADAMANTIUM

WP4 Planned Tasks

Perceptual PQoS Modelling. Intelligent audiovisual model for audiovisual over UMTS

networks.

Adaptation Mechanisms Computationally Intelligent EARLY adaptation mechanisms. Initial results very promising, a mechanism beyond

ADAMANTIUM

ICT-ADAMANTIUM

THANK YOU

ict-adamantium wp4 pqos models and adaptation mechanisms

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