This document describes the R2D2 research project funded by EPSRC to investigate network error control techniques for reliable data delivery. The project aims to design novel mathematical frameworks to optimize network-coded architectures for applications requiring ultra-reliable communications and energy efficiency. Specific research activities include optimizing 4G/5G systems for video multicasting, designing efficient rateless decoders, developing sparse network coding schemes, and novel coding schemes for relay networks. Initial results have been presented in 4 conference papers and 1 journal paper.

1. 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 - i.chatzigeorgiou@lancaster.ac.uk
{Postdoctoral Research Associate} Dr Andrea Tassi - a.tassi@lancaster.ac.uk
{Affiliated Member} Amjad Saeed Khan - a.khan9@lancaster.ac.uk
http://www.lancs.ac.uk/~chatzige/R2D2/
ataglance

2. 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

3. 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. 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

5. 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

6. 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
✴ So? Let’s simply use what we already have!

7. 4 G / 5 G S Y S T E M O P T I M I Z AT I O N
Photo credits: https://www.nasa.gov

8. 4 G / 5 G S Y S T E M O P T I M I Z AT I O N
• 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

9. 4 G / 5 G S Y S T E M O P T I M I Z AT I O N
Base Layer
Base + 1st Enhancement Layers
Base + 1st + 2nd Enhancement Layers
BS
QoS Zone 1QoS Zone 2QoS Zone 330% of UEs
60% of UEs
99% of UEs
Photo credits: http://www.animatedmoviewallpapers.com/
• We refer to H.264/SVC broadcast video streams

10. 4 G / 5 G S Y S T E M O P T I M I Z AT I O N
⊗⊗
⊕
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

11. 4 G / 5 G S Y S T E M O P T I M I Z AT I O N
Distance (m)
MaximumPSNRρ(dB)
90 110 130 150 170 190 210 230 250 270 2900
5
15
25
35
45
55
ˆt1ˆt2ˆt3
MrT
Heu. NO−SA
Heu. NO−MA
Heu. EW−MA
⌧ = 73
⌧ = 88
⌧ = 88
All the proposed
strategies meet the
coverage constraints
MrT
Classic NC
(NO-SA)
Code and Resource
Multiplexing
(EW-MA)
Resource
Multiplexing
(NO-MA)
PSNR
layers 1+2+3 PSNR
layers 1+2
PSNR
layers 1
• Minimization of bandwidth. User SLAs are constraints

12. 4 G / 5 G S Y S T E M O P T I M I Z AT I O N
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
45.845.8
45.8
45.8
45.8
35.9
35.9
35.9
35.9
35.9
35.9
35.9
35.9
35.9
35.9
35.9
27.9
27.9
27.9
27.9
27.9
27.9
27.9
27.9
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
39.939.9
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

13. 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!

14. 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
• 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/

15. 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
Probability of selecting zero
Delay
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
5
10
15
20
25
30
35
PER = 0
PER = 0.25
PER = 0.5
Sparsity increases
Decoding complexity decreases

16. 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 - i.chatzigeorgiou@lancaster.ac.uk
{Postdoctoral Research Associate} Dr Andrea Tassi - a.tassi@lancaster.ac.uk
{Affiliated Member} Amjad Saeed Khan - a.khan9@lancaster.ac.uk
http://www.lancs.ac.uk/~chatzige/R2D2/
ataglance