In the near future, the delivery of multimedia multicast services over next-generation networks is likely to become one of the main pillars of future cellular networks. In this extended abstract, we address the issue of efficiently multicasting layered video services by defining a novel optimization paradigm that is based on an Unequal Error Protection implementation of Random Linear Network Coding, and aims to ensure target service coverages by using a limited amount of radio resources.

1. Bristol, 5th February 2015
R2D2: Network error control for
Rapid and Reliable Data Delivery
Project supported by EPSRC under the
First Grant scheme (EP/L006251/1)
On Optimization of Network-coded
Scalable Multimedia Service
Multicasting
University of Bristol
Andrea Tassi and Ioannis Chatzigeorgiou
{a.tassi, i.chatzigeorgiou}@lancaster.ac.uk
andCommunications
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2. Starting Point and Goals
๏ Delivery of multimedia broadcast/multicast services over
4G/5G networks is a challenging task. This has propelled
research into delivery schemes.
๏ Multi-rate Transmission (MrT) 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
achieves a certain service level with at least a given probability
๏ Resource optimisation - Reduce the total amount of radio
resources needed to deliver a layered service.
2
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3. Index
1. System Parameters and Performance Analysis
2. Multi-Channel Resource Allocation Models
and Heuristic Strategies
3. Analytical Results
4. Concluding Remarks
3
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4. 1. System Parameters and Performance Analysis
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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).
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6. 6
๏ Encoding performed over each service layer independently
from the others.
๏ 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
x = {x1, . . . , xK}
Non-Overlapping Layered RNC
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kl
xl = {xi}kl
i=1
nl kl y = {yj}nl
j=1
yj =
klX
i=1
gj,i xi
Coef:icients$of$the$
linear$combination$
are$selected$over$a$
:inite$:ield$of$size$q

7. 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$l
๏ The probability that user u recover the first l service layers is
DNO,l(n1,u, . . . , nL,u) = DNO,l(nu) =
lY
i=1
Pi(ni,u)
PEP
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8. 8
๏ The source node (i) linearly combines data packets belonging to
the same window, (ii) repeats this process for all windows, and
(iii) broadcasts each stream of coded packets over one or more
subchannels
Expanding Window Layered RNC
๏ We define the l-th window as the set of source packets
belonging to the first l service layers. Namely,
where
Xl
Xl ={xj}Kl
j=1
Kl =
Pl
i=1 ki
k1 k2 k3
x1 x2 xK. . .. . .
Exp. Win. 3
Exp. Win. 2
Exp. Win. 1
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9. Expanding Window Layered RNC
๏ Sums allow us to consider all the possible combinations of
received coded packets
9
๏ The probability of user u recovering the first l layers
(namely, the l-th window) can be written as
DEW,l
L,u) =
=DEW,l(Nu)
=
N1,u
X
r1=0
· · ·
Nl 1,u
X
rl 1=0
Nl,u
X
rl=rmin,l
✓
N1,u
r1
◆
· · ·
✓
Nl,u
rl
◆
p
Pl
i=1(Ni,u ri)
(1 p)
Pl
i=1 ri
gl(r)
Prob.$of$receiving $out$
of$ coded$symbols
Prob.$of$decoding$
window$l
DEW,l(Nu)
DEW,l(N1,u, . . . , NL,u) =
=DEW,l(Nu)
=
N1,u
X
r1=0
· · ·
Nl 1,u
X
rl 1=0
Nl,u
X
rl=rmin,l
✓
N1,u
r1
◆
· · ·
✓
Nl,u
rl
◆
p
Pl
i=1(Ni,u ri)
(
r = {r1, . . . , rl}
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10. 2. Multi-Channel Resource Allocation Models
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11. Allocation Patterns
11
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3
coded packets
from x1
coded packets
from x2
coded packets
from x3
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3
Separated$
Allocation$
Pattern
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12. 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
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13. NO-MA Model
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๏ Consider the variable . It is 1, if u
can recover the first l layers with a probability value
, otherwise it is 0.
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD
(NO-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
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)
No.$of$packets$of$layer$l$
delivered$over$cMinimization$of$
resource$footprint
ˆB3
ˆB2
ˆB1
subch. 1subch. 2subch. 3

14. NO-MA Model
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๏ Consider the variable . It is 1, if u
can recover the first l layers with a probability value
, otherwise it is 0.
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD
(NO-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
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)
Each$service$level$shall$be$
achieved$by$a$predetermined$
fraction$of$users
No.$of$users
Target$fraction$of$users
(NO-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
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)
ˆB3
ˆB2
ˆB1
subch. 1subch. 2subch. 3

15. NO-MA Model
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๏ Consider the variable . It is 1, if u
can recover the first l layers with a probability value
, otherwise it is 0.
u,l = I
⇣
DNO,l(nu) ˆD
⌘
ˆD
(NO-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
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)
DynamicH$and$
systemHrelated$
constraints
(NO-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
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)
(NO-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
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)
ˆB3
ˆB2
ˆB1
subch. 1subch. 2subch. 3

16. NO-MA Heuristic
๏ The NO-MA 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
( )
13
m1, . . . , mC
n(1,c)
, . . . , n(L,c)
๏ The first step selects the
value of such that packets
delivered through subch. c are
received (at least with a target
prob.) by users.
mc
|U(mc)
| U · ˆtc
apping Resource Allocation Strategies
system where the source node delivers the
by means of the NO RNC principle. From (??),
indication variable u,l as follows:
u,l = I
⇣
DNO,l(nu) ˆD
⌘
. (13)
s, u,l = 1, if u can recover the first l layers
ity value that is equal to or greater than a target
wise u,l = 0.
e allocation model that we propose for the
Step 1 Subchannel MCSs optimization.
1: c C
2: v mMAX and
3: while c 1 do
4: repeat
5: mc v
6: v v 1
7: until |U(mc)
| U · ˆtc or v < mmin
8: c c 1
9: end while
because of the nature of the considered a
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17. NO-MA Heuristic
๏ The idea behind the second step can be summarised as follows
14
ˆB3
ˆB2
ˆB1
subchannel 1
subchannel 2
subchannel 3
DNO,1(n(1)
) ˆD DNO,2(n(1)
, n(2)
) ˆD
DNO,3(n(1)
, n(2)
, n(3)
) ˆD
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18. NO-MA Heuristic
๏ The idea behind the second step can be summarised as follows
14
e node delivers the
principle. From (6),
follows:
ˆD
⌘
. (13)
ver the first l layers
greater than a target
we propose for the
A (NO-SA) can be
(l,c)
(14)
= 1, . . . , L (15)
= 2, . . . , L (16)
= 1, . . . , C (17)
or l 6= c (18)
nts the overall num-
d to deliver all the L
1: c C
2: v mMAX and
3: while c 1 do
4: repeat
5: mc v
6: v v 1
7: until |U(mc)
| U · ˆtc or v < mmin
8: c c 1
9: end while
Step 2 Coded packet allocation for a the NO-MA case.
1: c 1
2: n(l,c)
1 for any l = 1, . . . , L and c = 1, . . . , C
3: n = {n(l)
}L
l=1, where n(l)
1 for any l = 1, . . . , L
4: for l 1, . . . , L do
5: while DNO,l(n) < ˆD and c  C do
6: n(l,c)
n(l,c)
+ 1
7: n(l) PC
t=1 n(l,t)
for any l = 1, . . . , L
8: if
PL
t=1 n(t,c)
= ˆBc then
9: c c + 1
10: end if
11: end while
12: if DNO,l(n) < ˆD and c > C then
13: no solution can be found.
14: end if
15: end for
because of the nature of the considered allocation pattern.
Furthermore, without loss of generality, we assume that coded
Requires$a$no.$of$steps

PC
t=1
ˆBt
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19. 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
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k1 k2 k3
x1 x2 xK. . .. . .
Exp. Win. 3
Exp. Win. 2
Exp. Win. 1
ˆB3
ˆB2
ˆB1
subch. 1subch. 2subch. 3

20. EW-MA Model
๏ The RA problem for the EW-MA 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
previously proposed heuristic strategy can be still applied.
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ˆB3
ˆB2
ˆB1
subch. 1subch. 2subch. 3

21. “Egalitarian” Model
17
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๏ Previous strategies ensure minimum SLA and minimize the
resource footprint. Point of view of the ISP…
๏ Best practice for burglars - To still object with the maximum
value and the minimum weight. The profit-cost ratio is
maximized.
๏ We define the model for a SA pattern as:
(E-SA) maximize
m1,...,mL
N(1),...,N(L)
UX
u=1
LX
l=1
Qu,l
, LX
l=1
N(l)
(1)
subject to
UX
u=1
Qu,l U ˆtl l = 1, . . . , L (2)
0  N(l)
 ˆBl l = 1, . . . , L. (3)
Pro(it$H$No.$of$video$layers$
recovered$by$any$of$the$users
Cost$H$No.$of$
transmissions$needed
SLAHrelated$constraint
๏ We can refer to the previous heuristics.
ˆB3
ˆB2
ˆB1
subch. 1subch. 2subch. 3

22. 3. Analytical Results
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23. Analytical Results (part 1)
19
๏ LTE-A eMBMS scenarios
๏ We compared the proposed strategies with a classic Multi-
rate Transmission strategy
๏ System performance was evaluated in terms of
It$is$a$maximization$of$the$
sum$of$the$user$QoS
Resource$footprint
=
8
>>>><
>>>>:
LX
l=1
CX
c=1
n(l,c)
, for NO-RNC
LX
l=1
CX
c=1
N(l,c)
, for EW-RNC.
max
m1,...,mL
UX
u=1
PSNRu
No$error$control$strategies$
are$allowed$(ARQ,$RLNC,$etc.)
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24. Analytical Results (part 1)
19
๏ LTE-A eMBMS scenarios
๏ We compared the proposed strategies with a classic Multi-
rate Transmission strategy
๏ System performance was evaluated in terms of
It$is$a$maximization$of$the$
sum$of$the$user$QoS
PSNR$after$recovery$of$the$basic$
and$the$:irst$l$enhancement$layers
⇢(u) =
8
><
>:
max
l=1,...,L
n
PSNRl D
(u)
NO,l
o
, for NO-RNC
max
l=1,...,L
n
PSNRl D
(u)
EW,l
o
, for EW-RNC
max
m1,...,mL
UX
u=1
PSNRu
No$error$control$strategies$
are$allowed$(ARQ,$RLNC,$etc.)
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25. Target cellTarget MG
eNB
Scenario$with$a$high$heterogeneity.$80$
UEs$equally$spaced$and$$placed$along$the$
radial$line$representing$the$symmetry$
axis$of$one$sector$of$the$target$cell
Analytical Results (part 1)
20
We$considered$Stream$A$and$B$
which$have$3$layers,$bitrate$of$
A$is$smaller$than$that$of$B
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26. Distance (m)
MaximumPSNRρ(dB)
90 110 130 150 170 190 210 230 250 270 290
0
5
15
25
35
45
55
ˆt1ˆt2ˆt3
MrT
Heu. NOW−MA
Heu. EW−MA
⌧ = 60
⌧ = 43
Analytical Results (part 1)
21
Stream$A
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All$the$proposed$
strategies$meet$
the$coverage$
constraints
EWHMA
NOHMA
MrT
PSNR
layers$1+2+3
PSNR
layers$1+2 PSNR
layers$1

27. Distance (m)
MaximumPSNRρ(dB)
90 110 130 150 170 190 210 230 250 270 290
0
5
15
25
35
45
55
ˆt1ˆt2ˆt3
MrT
Heu. NOW−MA
Heu. EW−MA
⌧ = 73
⌧ = 88
Analytical Results (part 1)
22
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All$the$proposed$
strategies$meet$
the$coverage$
constraints
MrT
EWHMA
NOHMA
PSNR
layers$1+2+3
PSNR
layers$1+2 PSNR
layers$1
Stream$B

28. Analytical Results (part 2)
23
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๏ LTE-A allows multiple contiguous BS to deliver (in a
synchronous fashion) the same services by means of the
same signals
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
Spacial$SINR$distribution
Single$Frequency$Network
4HBS$SFN,$1700$users$placed$at$
the$vertices$of$a$regular$square$
grid$placed$on$the$playground.$

29. Analytical Results (part 2)
24
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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
3Hlayer$
stream
๏ Also in this case MrT cannot ensure the desired coverage!

30. 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 4Hlayer$
stream
Analytical Results (part 2)
24
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๏ Also in this case MrT cannot ensure the desired coverage!

31. 4. Concluding Remarks
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32. Concluding Remarks
26
๏ Definition of a generic system model that can be easily
adapted to practical scenarios and different viewpoints (ISP
vs users).
๏ 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
good quality solutions in a finite number of steps.
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33. Thank you for
your attention
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For more information
http://arxiv.org/abs/1411.5547
or
http://goo.gl/Z4Y9YF
A. Tassi, I. Chatzigeorgiou, and D. Vukobratović, “Resource Allocation
Frameworks for Network-coded Layered Multimedia Multicast Services”,
IEEE Journal on Selected Areas in Communications, Special Issue on
“Fundamental Approaches to Network Coding in Wireless Communication
Systems”, in press.

34. Bristol, 5th February 2015
R2D2: Network error control for
Rapid and Reliable Data Delivery
Project supported by EPSRC under the
First Grant scheme (EP/L006251/1)
On Optimization of Network-coded
Scalable Multimedia Service
Multicasting
University of Bristol
Andrea Tassi and Ioannis Chatzigeorgiou
{a.tassi, i.chatzigeorgiou}@lancaster.ac.uk
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