R2D2 Project (EP/L006251/1) - Research Objectives & Outcomes

R2D2 A PROJECT FUNDED BY EPSRC UNDER THE FIRST GRANT SCHEME

E P / L 0 0 6 2 5 1 / 1

NETWORK ERROR CONTROL FOR RAPID AND RELIABLE DATA DELIVERY{Research Objectives & Outcomes}{Principal Investigator} Dr Ioannis Chatzigeorgiou - [email protected] {Postdoctoral Research Associate} Dr Andrea Tassi - [email protected]{Affiliated Member} Amjad Saeed Khan - [email protected]

http://www.lancs.ac.uk/~chatzige/R2D2/

at a glance

mailto:[email protected]




A I M S & A S P I R AT I O N S

• 18-month EPSRC research project on network error control aspects

• Design novel mathematical frameworks - To identify key relationships between system and transmission parameters, understanding network dynamics and optimise network-coded architectures

• Delve into practical applications - Ultra-reliable communications, delay constrained applications, green and energy-efficiency architectures

R E S E A R C H A C T I V I T I E S

• 4G and 5G Cross-Layer System Optimization for Video Multicasting

• Design of On-the-fly Rateless Decoders

• Sparse Network Coding Schemes with Minimum Decoding Complexity

• Novel Network Coding Schemes for Relay Networks

✴ So far, results have been presented in 4 conference and 1 journal papers

4 G / 5 G S Y S T E M O P T I M I Z AT I O N

• Multimedia multicast services are becoming a challenge for service providers

• Video content delivery represented 53% of the global mobile Internet traffic in 2013 and is expected to rise to 67% by 2018

• LTE-Advanced allows multicast and broadcast communications via the eMBMS framework

• Modern video compression standards (such as, H.264/AVC, H.264/SVC, H.265) allow the generation of scalable video contents


• Multimedia multicast services are becoming a challenge for service providers

• Video content delivery represented 53% of the global mobile Internet traffic in 2013 and is expected to rise to 67% by 2018

• LTE-Advanced allows multicast and broadcast communications via the eMBMS framework

• Modern video compression standards (such as, H.264/AVC, H.264/SVC, H.265) allow the generation of scalable video contents

✴ So? Let’s simply use what we already have!


Photo credits: https://www.nasa.gov


• Too many system- and transmission-related parameters that can be tuned

• What does the Service Provider want? To meet Service-Level Agreements SLAs with the minimum amount of radio resources

• What does the user want? To get an acceptable uninterruptible user experience

Photo credits: https://www.nasa.gov


Base Layer

Base + 1st Enhancement Layers

Base + 1st + 2nd Enhancement Layers

BS

QoS Zone 1QoS Zone 2QoS Zone 330% of UEs60% of UEs

99% of UEsPhoto credits: http://www.animatedmoviewallpapers.com/

• We refer to H.264/SVC broadcast video streams


⊗⊗⊕

x1 xk1 xK. . .. . .

yj

xk2

Source message

Coded packets

gj,2gj,1

Photo credits: http://www.animatedmoviewallpapers.com/

• We refer to H.264/SVC broadcast video streams

• Each layer is broadcast via a Random Linear Network Coding (RLNC) strategy


Distance (m)

Maxim

um

PSNR

ρ(d

B)

90 110 130 150 170 190 210 230 250 270 2900

5

15

25

35

45

55

t̂1t̂2t̂3

MrT

Heu. NO−SA

Heu. NO−MA

Heu. EW−MA

⌧ = 73

⌧ = 88⌧ = 88

All the proposed strategies meet the

coverage constraintsMrT

Classic NC(NO-SA)

Code and Resource Multiplexing

(EW-MA)

Resource Multiplexing

(NO-MA)

PSNRlayers 1+2+3 PSNR

layers 1+2

PSNRlayers 1

• Minimization of bandwidth. User SLAs are constraints


x position (m)

yposition(m

)

−500 −300 −100 100 300 500 700

−200

−100

0

100

200

300

400

500

600

700

x position (m)

yposition(m

)

−500 −300 −100 100 300 500 700

−200

−100

0

100

200

300

400

500

600

700

45.8

45.8

45.8

45.8

45.8

45.8

45.8

45.8

45.845.

8

35.9

35.9

35.9

35.9

35.9

35.9

35.9 35.9

35.9

35.935.9

27.9

27.9

27.9 27.9

27.9

27.9

27.927.9

27.9

27.9

27.9

27.9

E"SAMrT

45.8

45.8

x position (m)yposition(m

)−500 −300 −100 100 300 500 700

−200

−100

0

100

200

300

400

500

600

700

x position (m)yposition(m

)−500 −300 −100 100 300 500 700

−200

−100

0

100

200

300

400

500

600

700

46.4

46.4

46.4

46.4

46.4

46.4

46.4

46.4

46.4

46.4

46.4

46.4

39.9

39.9

39.9

39.9

39.9

39.9

39.9

39.9

39.9

39.9

39.9

33.4

33.4

33.4

33.4

33.4

33.4

33.4

33.4

28.1

28.1 28.1

28.1

28.1

28.1

28.1

28.1

28.1

46.4

46.4

E"SAMrT

• Maximization of the system profit-cost ratio, i.e., no. of video layers recovered by users over the bandwidth used.

• User SLAs are used as constraints

S PA R S E N E T W O R K C O D I N G S T R AT E G I E S

• What is the price of the RLNC simplicity? The computational complexity of the decoder

• The decoding complexity depends on algebraic features of the code (finite filed size) and the number of source packets forming (on average) each coded packet

✴ So? Let’s reduce the linear combination degree!


• This problem involves sparse random matrices…

• Since 1997, only 4 papers used accurate methods in order to shed some light onto the topic

• Engineering approach: Let’s put some bounds!

Photo credits: http://curvaturasvariantes.com/


Probabi l i ty of selecting zero

Delay

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.95

10

15

20

25

30

35

PER = 0PER = 0 .25PER = 0 .5

Sparsity increases

Decoding complexity decreases

R2D2 A PROJECT FUNDED BY EPSRC UNDER THE FIRST GRANT SCHEME

E P / L 0 0 6 2 5 1 / 1

NETWORK ERROR CONTROL FOR RAPID AND RELIABLE DATA DELIVERY{Research Objectives & Outcomes}{Principal Investigator} Dr Ioannis Chatzigeorgiou - [email protected] {Postdoctoral Research Associate} Dr Andrea Tassi - [email protected]{Affiliated Member} Amjad Saeed Khan - [email protected]


at a glance





R2D2 Project (EP/L006251/1) - Research Objectives & Outcomes

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