The explosive growth of content-on-the-move, such as video streaming to mobile devices, has propelled research on multimedia broadcast and multicast schemes. Multi-rate transmission strategies have been proposed as a means of delivering layered services to users experiencing different downlink channel conditions. In this presentation, we consider random linear network coding for its inherent reliability features and study two encoding approaches, which are appropriate for layered services. We derive packet error probability expressions and use them as performance metrics in the formulation of resource allocation frameworks. The aim of these frameworks is both the optimization of the transmission scheme and the minimization of the number of broadcast packets on each downlink channel, while offering service guarantees to a predetermined fraction of users. Our proposed frameworks are adapted to the LTE stack and the integrated eMBMS technology. We focus on the delivery of a video service based on the H.264/SVC standard and demonstrate the advantages of layered network coding over multi-rate transmission. Furthermore, we establish that the choice of both the network coding technique and the resource allocation method play a critical role in the footprint of a service, as determined by the quality of each received video layer.

1. London, 9th June 2014
R2D2: Network error control for
Rapid and Reliable Data Delivery
Project supported by EPSRC under the
First Grant scheme (EP/L006251/1)
Resource Allocation
Frameworks for Network-coded
Layered Multimedia Multicast
Services
UCL
Andrea Tassi*, Ioannis Chatzigeorgiou* and
Dejan Vukobratović+
+Dep. of Power, Electronics and
Communication Eng., Univ. of Novi Sad
dejanv@uns.ac.rs
* School of Computing and
Communications, Lancaster University
{a.tassi, i.chatzigeorgiou}@lancaster.ac.uk

2. Starting Point and Goals
๏ Delivery of multimedia broadcast/multicast services over 4G
networks is a challenging task. This has propelled research into
delivery schemes.
๏ Multi-rate transmission strategies have been proposed as a
means of delivering layered services to users experiencing
different downlink channel conditions.
๏ Layered service consists of a basic layer and multiple
enhancement layers.
Goals
๏ Error control - Ensure that a predetermined fraction of users
achieve a certain service level with at least a given probability
๏ Resource optimisation - Minimise the total amount of radio
resources needed to deliver a layered service.
2

3. Index
1. System Parameters and Performance Analysis
2. Multi-Channel Resource Allocation Models
and Heuristic Strategies
3. H.264/SVC Service Delivery over LTE-A
eMBMS Networks
4. Analytical Results
5. Concluding Remarks and Future Extensions
3

4. 1. System Parameters and Performance Analysis

5. System Model
๏ One-hop wireless communication system composed of one
source node and U users
5
UE 1
UE 3
UE 2
UE 4
UE U
Source
Node
ˆB3
ˆB2
ˆB1
subch. 1
subch. 2
subch. 3
๏ Each PtM layered service is delivered through C orthogonal
broadcast erasure subchannels
The$same$MCS
Capacity$of$subch.$3$
(no.$of$packets)
๏ Each subchannel delivers streams of (en)coded packets
(according to the RLNC principle).

6. 6
k1 k2 k3
x1 x2 xK. . .. . .
๏ is a layered source message of K source
packets, classified into L service layers (packets are arranged
in order of decreasing importance)
x = {x1, . . . , xK}
Non-Overlapping Layered RNC

7. 6
๏ The source node will linearly combine the data packets
composing the l-th layer and will generate a
stream of coded packets , where
k1 k2 k3
x1 x2 xK. . .. . .
๏ is a layered source message of K source
packets, classified into L service layers (packets are arranged
in order of decreasing importance)
x = {x1, . . . , xK}
Non-Overlapping Layered RNC
kl
xl = {xi}kl
i=1
nl kl y = {yj}nl
j=1
yj =
klX
i=1
gj,i xi
Random$coef<icient$
belonging$to$a$f.f.$Fq

8. Non-Overlapping Layered RNC
๏ User u recovers layer l if it will collect k_l linearly independent
coded packets. The prob. of this event is
7
kl
Pl(nl,u) =
nl,u
X
r=kl
✓
nl,u
r
◆
pnl,u r
(1 p)r
h(r)
=
nl,u
X
r=kl
✓
nl,u
r
◆
pnl,u r
(1 p)r
kl 1Y
i=0
h
1
1
qr i
i
| {z }
h(r)
Prob.$of$receiving$r$out$of$nl,u$coded$symbols
Prob.$of$decoding$
layer$l
๏ The probability that user u recover the first l service layers is
DNO,l(n1,u, . . . , nL,u) = DNO,l(nu) =
lY
i=1
Pi(ni,u)
PEP

9. 8
๏ The source node (i) linearly combines data packets belonging to
the same window, (ii) repeats this process for all windows, and
(iii) broadcasts each stream of coded packets over one or more
subchannels
Expanding Window Layered RNC
๏ We define the l-th window as the set of source packets
belonging to the first l service layers. Namely,
where
Xl
Xl ={xj}Kl
j=1
Kl =
Pl
i=1 ki
k1 k2 k3
K3
K2
K1
x1 x2 xK. . .. . .

10. Expanding Window Layered RNC
9
๏ The probability of user u recovering the first l layers
(namely, the l-th window) can be written as
DEW,l
L,u) =
=DEW,l(Nu)
=
N1,u
X
r1=0
· · ·
Nl 1,u
X
rl 1=0
Nl,u
X
rl=rmin,l
✓
N1,u
r1
◆
· · ·
✓
Nl,u
rl
◆
p
Pl
i=1(Ni,u ri)
(1 p)
Pl
i=1 ri
gl(r)
Prob.$of$receiving $out$
of$ coded$symbols
Prob.$of$decoding$
window$l
rl
DEW,l(Nu)
DEW,l(N1,u, . . . , NL,u) =
=DEW,l(Nu)
=
N1,u
X
r1=0
· · ·
Nl 1,u
X
rl 1=0
Nl,u
X
rl=rmin,l
✓
N1,u
r1
◆
· · ·
✓
Nl,u
rl
◆
p
Pl
i=1(Ni,u ri)
(
r = {r1, . . . , rl}

11. Expanding Window Layered RNC
๏ Sums allow us to consider all the possibilities of
9
๏ The probability of user u recovering the first l layers
(namely, the l-th window) can be written as
DEW,l
L,u) =
=DEW,l(Nu)
=
N1,u
X
r1=0
· · ·
Nl 1,u
X
rl 1=0
Nl,u
X
rl=rmin,l
✓
N1,u
r1
◆
· · ·
✓
Nl,u
rl
◆
p
Pl
i=1(Ni,u ri)
(1 p)
Pl
i=1 ri
gl(r)
Prob.$of$receiving $out$
of$ coded$symbols
Prob.$of$decoding$
window$l
rl
DEW,l(Nu)
DEW,l(N1,u, . . . , NL,u) =
=DEW,l(Nu)
=
N1,u
X
r1=0
· · ·
Nl 1,u
X
rl 1=0
Nl,u
X
rl=rmin,l
✓
N1,u
r1
◆
· · ·
✓
Nl,u
rl
◆
p
Pl
i=1(Ni,u ri)
(
r = {r1, . . . , rl}

12. 2. Multi-Channel Resource Allocation Models and
Heuristic Strategies

13. Allocation Patterns
11
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3

14. Allocation Patterns
11
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3
coded packets
from x1
coded packets
from x2
coded packets
from x3
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3
Separated$
Allocation$
Pattern

15. Mixed$
Allocation$
Pattern
ˆB3
ˆB2
ˆB1
coded packets
from x3 or X3
coded packets
from x2 or X2
coded packets
from x1 or X1
subchannel 1
subchannel 2
subchannel 3
Allocation Patterns
11
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3

16. NO-SA Model
๏ Consider the indication variable
It is 1, if u can recover the first l layers with a probability value
, otherwise it is 0.
12
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD

17. NO-SA Model
๏ Consider the indication variable
It is 1, if u can recover the first l layers with a probability value
, otherwise it is 0.
๏ The RA problem for the NO-SA case is
12
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD
(NO-SA) min
m1,...,mC
n(1,c),...,n(L,c)
LX
l=1
CX
c=1
n(l,c)
(1)
subject to
UX
u=1
u,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
n(l,c)
 ˆBc c = 1, . . . , C (4)
n(l,c)
= 0 for l 6= c (5)
No.$of$packets$of$layer$l$
delivered$over$c
Minimization$of$
resource$footprint

18. NO-SA Model
๏ Consider the indication variable
It is 1, if u can recover the first l layers with a probability value
, otherwise it is 0.
๏ The RA problem for the NO-SA case is
12
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD
(NO-SA) min
m1,...,mC
n(1,c),...,n(L,c)
LX
l=1
CX
c=1
n(l,c)
(1)
subject to
UX
u=1
u,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
n(l,c)
 ˆBc c = 1, . . . , C (4)
n(l,c)
= 0 for l 6= c (5)
Each$service$level$shall$be$
achieved$by$a$predetermined$
fraction$of$users
No.$of$users
Target$fraction$of$users

19. NO-SA Model
๏ Consider the indication variable
It is 1, if u can recover the first l layers with a probability value
, otherwise it is 0.
๏ The RA problem for the NO-SA case is
12
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD
(NO-SA) min
m1,...,mC
n(1,c),...,n(L,c)
LX
l=1
CX
c=1
n(l,c)
(1)
subject to
UX
u=1
u,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
n(l,c)
 ˆBc c = 1, . . . , C (4)
n(l,c)
= 0 for l 6= c (5)
DynamicI$and$
systemIrelated$
constraints
Because$of$the$SA$
pattern

20. NO-SA Heuristic
๏ The NO-SA is an hard integer optimisation problem because
of the coupling constraints among variables
๏ We propose a two-step heuristic strategy
i. MCSs optimisation ( )
ii. No. of coded packet per-subchannel optimization
( )
๏ The heuristic can provide a solution after a finite number of
steps
๏ It can be used in real time contexts.
13
m1, . . . , mC
n(1,c)
, . . . , n(L,c)

21. NO-MA Model
๏ The NO-SA problem can be easily extended to the MA pattern
by removing the last constraint
14
(NO-SA) min
m1,...,mC
n(1,c),...,n(L,c)
LX
l=1
CX
c=1
n(l,c)
(1)
subject to
UX
u=1
u,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
n(l,c)
 ˆBc c = 1, . . . , C (4)
n(l,c)
= 0 for l 6= c (5)

22. NO-MA Model
๏ The NO-SA problem can be easily extended to the MA pattern
by removing the last constraint
14
(NO-SA) min
m1,...,mC
n(1,c),...,n(L,c)
LX
l=1
CX
c=1
n(l,c)
(1)
subject to
UX
u=1
u,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
n(l,c)
 ˆBc c = 1, . . . , C (4)
n(l,c)
= 0 for l 6= c (5)
(NO-MA)

23. NO-MA Model
๏ The NO-SA problem can be easily extended to the MA pattern
by removing the last constraint
14
(NO-SA) min
m1,...,mC
n(1,c),...,n(L,c)
LX
l=1
CX
c=1
n(l,c)
(1)
subject to
UX
u=1
u,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
n(l,c)
 ˆBc c = 1, . . . , C (4)
n(l,c)
= 0 for l 6= c (5)
(NO-MA)
๏ The NO-MA is still an hard integer optimisation problem. We
adapted the two-step heuristic strategy.

24. EW-MA Model
๏ We define the indicator variable
User u will recover the first l service layers (at least) with
probability if any of the windows l, l+1, …, L are recovered
(at least) with probability
15
µu,l = I
L_
t=l
n
DEW,t(Nu) ˆD
o
!
ˆD
ˆD
๏ Consider the EW delivery mode
k1 k2 k3
K3
K2
K1
x1 x2 xK. . .. . .

25. EW-MA Model
๏ The RA problem for the EW-SA case is
16
(EW-MA) min
m1,...,mC
N(1,c),...,N(L,c)
LX
l=1
CX
c=1
N(l,c)
(1)
subject to
UX
u=1
µu,l U ˆtl l = 1, . . . , L (2)
mc 1 < mc c = 2, . . . , L (3)
0 
LX
l=1
N(l,c)
 ˆBc c = 1, . . . , C (4)
No.$of$packets$of$
window$l$delivered$
over$c
๏ It is still an hard integer optimisation problem but the
proposed heuristic strategy can be still applied.

26. 3. H.264/SVC Service Delivery over eMBMS Networks

27. Layered Video Streams
Video streams formed by multiple video layers:
๏ the base layer - provides basic reconstruction quality
๏ multiple enhancement layers - which gradually improves the
quality of the base layer
18
Considering a H.264/SVC video stream
base
e1
e2
GoP
๏ it is a GoP stream
๏ a GoP has fixed number of
frames
๏ it is characterised by a time
duration (to be watched)
๏ it has a layered nature

28. H.264/SVC and NC
๏ The decoding process of a H.264/SVC service is performed on a
GoP-basis
19
k1 k2 k3
K3
K2
K1
x1 x2 xK. . .. . .
๏ Hence, the can be defined as
The$basic$layer$
of$a$GoP
1st$enhancement$
layer$of$a$GoP
2nd$enhancement$
layer$of$a$GoP
kl =
⌃Rl dGoP
H
⌥
kl
Source/Coded$packet$
bit$size
Time$duration$of$a$
GoP
Bitrate$of$the$video$
layer

29. LTE-A System Model
radio frame
time
frequency
TB left for other services
eMBMS-capable subframes
TB of subchannel 1 TB of subchannel 2 TB of subchannel 3
๏ PtM communications managed by the eMBMS framework
๏ We refer to a SC-eMBMS system where a eNB delivers a
H.264/SVC video service formed by L different layers to the
target MG
๏ The first and the L-th layers represents the basic and L-1
H.264/SVC enhancement layers, respectively
20
TB$=$Coded$Packets

30. 3. Analytical Results

31. Analytical Results
22
๏ We compared the proposed strategies with a classic Multi-
rate Transmission strategy
๏ System performance was evaluated in terms of
Resource$footprint QoS
PSNR$after$recovery$of$the$basic$and$
the$<irst$l$enhancement$layers
It$is$a$maximisation$of$the$
sum$of$the$user$QoS
⇢(u) =
8
><
>:
max
l=1,...,L
n
PSNRl D
(u)
NO,l
o
, for NO-RNC
max
l=1,...,L
n
PSNRl D
(u)
EW,l
o
, for EW-RNC
=
8
>>>><
>>>>:
LX
l=1
CX
c=1
n(l,c)
, for NO-RNC
LX
l=1
CX
c=1
N(l,c)
, for EW-RNC.
max
m1,...,mL
UX
u=1
PSNRu

32. Target cellTarget MG
eNB
Scenario$with$a$high$
heterogeneity.$There$are$80$UEs$
placed$along$the$radial$line$
representing$the$symmetry$axis$
of$one$sector$of$the$target$cell
Analytical Results
23

33. ๏ A and B have 3 layers, bitrate of A is smaller than that of B
Finite field size q
TotalTBtransmissionsσ
2 22
24
26
2830
40
50
60
70
80
90
100 Dir. NO−SA, Stream A
Dir. NO−MA, Stream A
Heu. NO−SA and NO−MA, Stream A
Dir. EW−MA, Stream A
Heu. EW−MA, Stream A
Dir. NO−SA, Stream B
Dir. NO−MA, Stream B
Heu. NO−SA and NO−MA, Stream B
Dir. EW−MA, Stream B
Heu. EW−MA, Stream B
Footprint$of$opt.$and$heur.$
resource$allocation$solutions
The$performance$gap$
between$the$heuristic$and$the$
direct$solutions$is$negligible
Analytical Results
23

34. 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
= 60
= 60
= 43
Analytical Results
24
Stream$A$
q = 2
All$the$proposed$
strategies$meet$
the$coverage$
constraints
MrT
NOISA
EWIMA
NOIMA

35. Analytical Results
25
Stream$B$
q = 2
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
NOISA
EWIMA
NOIMA

36. ๏ The NO-MA and EW-MA strategies are equivalent both in
terms of resource footprint and service coverage
๏ The service coverage of NO-SA still diverges from that of
NO-MA and EW-MA.
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
Stream A
Stream B
Streams$A$and$B$
q = 256
Analytical Results
26

37. 4. Concluding Remarks and Future Extensions

38. Concluding Remarks
28
๏ Generic system model that can be easily adapted to practical
scenarios has been presented
๏ Derivation of the theoretical framework to assess user QoS
๏ Definition of efficient resource allocation frameworks, that
can jointly optimise both system parameters and the error
control strategy in use
๏ Development of efficient heuristic strategies that can derive
solutions in a finite number of steps.

39. Future Extensions
29
๏ LTE-A allows multiple contiguous BS to deliver (in a
synchronous fashion) the same services by means of the
same signals
๏ Users can combine multiple transmissions and does not need
of HO procedures.
eNB
eNB
eNB
eNB
M1/M2
MCE / MBMS-GW
SFN
3
2
1
B
UE3
UEMUE2
UE1
UE4
−400 −200 0 200 400 600
−200
−100
0
100
200
300
400
500
600
700
0
5
10
15
Distribution$of$the$maximum$
acceptable$user$MCSs
Single$Frequency$Network

40. Thank you for
your attention

41. London, 9th June 2014
R2D2: Network error control for
Rapid and Reliable Data Delivery
Project supported by EPSRC under the
First Grant scheme (EP/L006251/1)
Resource Allocation
Frameworks for Network-coded
Layered Multimedia Multicast
Services
UCL
Andrea Tassi*, Ioannis Chatzigeorgiou* and
Dejan Vukobratović+
+Dep. of Power, Electronics and
Communication Eng., Univ. of Novi Sad
dejanv@uns.ac.rs
* School of Computing and
Communications, Lancaster University
{a.tassi, i.chatzigeorgiou}@lancaster.ac.uk