This document discusses a proposed resource allocation model for delivering scalable video services over 4G/5G networks using network coding. The model aims to maximize the number of users achieving a certain quality of service level while minimizing radio resources. It formulates the problem as an integer optimization and proposes a heuristic algorithm to solve it. Results show the approach can improve service coverage by up to 2.5 times compared to conventional strategies.

1. London, 9th June 2015
R2D2
EPSRC First Grant
Scheme (EP/L006251/1)
Optimized Network-coded
Scalable Video Multicasting
over eMBMS Networks
IEEE ICC 2015 - Mobile and Wireless Networking Symposium
A. Tassi, I. Chatzigeorgiou, D. Vukobratović, A. L. Jones
a.tassi@{lancaster.ac.uk, bristol.ac.uk}
andCommunications
School of Computing

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.
๏ Scalable video service (such as H.264/SVC) consists of a basic
layer and multiple enhancement layers.
๏ Multi-rate Transmission (MrT) strategies have been proposed
as a means of delivering layered services to users experiencing
different downlink channel conditions.
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 in a fair way.
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3. Index
1. System Parameters and Performance Analysis
2. Proposed Resource Allocation Modeling and
Heuristic Solution
3. Analytical Results
4. Concluding Remarks
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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 a Single
Frequency Network (SFN) and a multicast group of U users
5
๏ All the BSs forming the SFN multicast the same layered video
service, in a synchronous fashion.
๏ Reliability ensured via the Unequal Error Protection RNC
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BS
BS
BS
BS
M1/M2
(MCE / MBMS-GW)
SFN
4
1
2
3
UE3
UEUUE2
UE1
UE4
LTE-A Core Network

6. 6
๏ Encoding: (i) Source elements belonging to the same window
are linearly combined, (ii) The process is repeated for all the
windows, and (iii) Each stream of coded packets is multicast.
Unequal Error Protection RNC
๏ We define the -th window as the set of source packets
belonging to the first l service layers. Namely,
where
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k1 k2 k3
x1 x2 xK. . .. . .
Window 3 of K3 source elements
Win. 2 of K2 src el.
Win. 1 of K1 src el.
` x1:`
x1:` = {xj}K`
j=1
K` =
P`
i=1 ki

7. 7
UEP-RNC and LTE-A
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Data$stream
associated$with$$ $
⊗⊗ ⊗
⊕
TB
MACPHY
Data$stream
associated$with$$ $
MAC$PDU
associated
with
x1 x2 xK. . .
gj,1 gj,2 gj,K2
. . . xK2
y1 yj. . . . . .
Source$Message
. . .
yj
v1 v2
v2
Service'layers'
arrive'at'the'MAC'
layer
Coded'elements'
are'generated
Coded'elements'
are'mapped'onto'
one'or'more'
PDUs'
๏ Window is always transmitted with the MCS
๏ Coded elements of different windows cannot be mixed within a PDU
m``

8. Performance Model
8
๏ A user recovers the first layers (namely, the -th window) if
it collects linearly independent coded elements from
windows , which occurs with probability
Prob.'of'receiving ''''''out'
of' PDUs
Prob.'of'decoding'window'''''
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u `
K`
1, . . . , `
`
Pu(N1:`)=
N1X
r1=0
· ·
N`X
r`=0
`Y
i=1
✓
Ni
ri
◆
(1 pu,i)ri
pNi ri
u,i g(r1:`)
N1:` = {N1, . . . , N`}
`
✴ A. Tassi et al., “Resource-Allocation Frameworks for Network-
Coded Layered Multimedia Multicast Services”, IEEE J. Sel.
Areas Commun., vol. 33, no. 2, Feb. 2015
Sums'exploit'all'the'
combinations'of'received'
coded'elements
r1:` = {r1, . . . , r`}

9. 2. Proposed Resource Allocation Modelling and
Heuristic Solution
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10. Problem Formulation
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๏ We define the indicator variable
User will recover the first service layers (at least) with
probability if any of the windows are recovered
(at least) with probability .
u,` = I
L_
i=`
Pu(N1:i) ˆQ
!
.
u `
ˆQ `, ` + 1, L
ˆQ
(UEP-RAM) max
m1,...,mL
N1,...,NL
UX
u=1
LX
`=1
u,`
. LX
`=1
N` (1)
subject to
UX
u=1
u,` U ˆt` ` = 1, . . . , L (2)
0  N`  ˆN` ` = 1, . . . , L. (3)

11. Problem Formulation
10
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๏ We define the indicator variable
User will recover the first service layers (at least) with
probability if any of the windows are recovered
(at least) with probability .
u,` = I
L_
i=`
Pu(N1:i) ˆQ
!
.
u `
ˆQ `, ` + 1, L
ˆQ
(UEP-RAM) max
m1,...,mL
N1,...,NL
UX
u=1
LX
`=1
u,`
. LX
`=1
N` (1)
subject to
UX
u=1
u,` U ˆt` ` = 1, . . . , L (2)
0  N`  ˆN` ` = 1, . . . , L. (3)
Pro$it'@'No.'of'video'layers'
recovered'by'any'of'the'users
Cost'@'No.'of'
transmissions'needed

12. Problem Formulation
10
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๏ We define the indicator variable
User will recover the first service layers (at least) with
probability if any of the windows are recovered
(at least) with probability .
u,` = I
L_
i=`
Pu(N1:i) ˆQ
!
.
u `
ˆQ `, ` + 1, L
ˆQ
(UEP-RAM) max
m1,...,mL
N1,...,NL
UX
u=1
LX
`=1
u,`
. LX
`=1
N` (1)
subject to
UX
u=1
u,` U ˆt` ` = 1, . . . , L (2)
0  N`  ˆN` ` = 1, . . . , L. (3)
Pro$it'@'No.'of'video'layers'
recovered'by'any'of'the'users
Cost'@'No.'of'
transmissions'needed
Each'service'level'
shall'be'achieved'by'
a'predetermined'
fraction'of'users
Target'fraction'of'users
…'within'a'certain'time.

13. Problem Heuristic
๏ Users provide as CQI feedback the max. MCS index ensuring
an acceptable PDU error probability (at the MAC Layer). The
larger the MCS index is, the higher the modulation order or the
lower the error-correcting capability is.
๏ The UEP-RAM is an hard integer optimisation problem
because of the coupling constraints among variables. We
proposed the following heuristic strategy.
๏ Remark Assume that the first windows are not
delivered. The service coverage can only be offered if
(i) service layers are recovered with a
probability of at least by the user fraction .
(ii) and the remaining layers are recovered as stated in SLA.
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s 2 [0, L 1]
1, . . . , (s + 1)
ˆQ ˆt1

14. Heuristic Procedure
1. The value of is initially set to . We try to transmit only
window
2. MCS of window is accommodated via
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and the PDU transmission are optimized via
3. If any of these problems cannot be solved, is decreased and
the procedure repeated. Otherwise, the solution is refined.
s
L 1
L
` 2 [s + 1, L]
s
(S1-`) arg max
m`2[1,15]
n UX
u=1
I
⇣
m`  m(u)
⌘
U t0
`
o
(S2-`) arg min
N`2[0, ˆN`]
n
P(N1:`) ˆQ ^ 0  N`  ˆN`
o
Requires'a'
Ginite'no.'
of'steps
MCSs'and'PDU'
trans.'indep.'
optimized'across'
the'windows'

15. Heuristic Procedure
1. The value of is initially set to . We try to transmit only
window
2. MCS of window is accommodated via
12
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and the PDU transmission are optimized via
3. If any of these problems cannot be solved, is decreased and
the procedure repeated. Otherwise, the solution is refined.
s
L 1
L
` 2 [s + 1, L]
s
(S1-`) arg max
m`2[1,15]
n UX
u=1
I
⇣
m`  m(u)
⌘
U t0
`
o
(S2-`) arg min
N`2[0, ˆN`]
n
P(N1:`) ˆQ ^ 0  N`  ˆN`
o
Requires'a'
Ginite'no.'
of'steps
MCSs'and'PDU'
trans.'indep.'
optimized'across'
the'windows'
Total'no.'of'
windows

16. Heuristic Procedure
1. The value of is initially set to . We try to transmit only
window
2. MCS of window is accommodated via
12
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School of Computing
and the PDU transmission are optimized via
3. If any of these problems cannot be solved, is decreased and
the procedure repeated. Otherwise, the solution is refined.
s
L 1
L
` 2 [s + 1, L]
s
(S1-`) arg max
m`2[1,15]
n UX
u=1
I
⇣
m`  m(u)
⌘
U t0
`
o
(S2-`) arg min
N`2[0, ˆN`]
n
P(N1:`) ˆQ ^ 0  N`  ˆN`
o
Requires'a'
Ginite'no.'
of'steps
MCSs'and'PDU'
trans.'indep.'
optimized'across'
the'windows'
No.'of'windows'
to'be'skipped'
Total'no.'of'
windows

17. 3. Analytical Results
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18. Numerical Results
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๏ SFN scenario composed by 4 BSs
๏ We compared the proposed strategies with a classic Multi-
rate Transmission (MrT) strategy
System'proGit
No'error'control'
strategies'are'allowed'
(ARQ,'RLNC,'etc.)
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max
m1,...,mL
UX
u=1
LX
`=1
˜u,`

19. Numerical Results
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๏ SFN scenario composed by 4 BSs, 1700 users placed at the
vertices of a regular square grid placed on the playground
๏ We considered a 3-layer and a 4-layer video streams.
eNB
eNB
eNB
eNB
M1/M2
MCE / MBMS-GW
SFN
3
2
1
B
UE3
UEMUE2
UE1
UE4

20. 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
3
33
3
3
33 3
3
3
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
UEP$RAM
MrT
3
3
Heuris/c
Numerical Results
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3@layer'stream
QoS'level'3'
achieved'by'
34.1%'vs.'60%'
of'the'users'
QoS'level'3'
achieved'by'
74%'vs.'60%'
of'the'users'
SFN'BS
QoS'level'3
QoS'level'2
QoS'level'1

21. 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
MrT
4
44
4
4
4 4
4
44
4
4
3
3
3
3
3
3
3
3
33
3
22
2
2
2
2
2
2
1
1
1
1
1
1
1
1 1
4
4
UEP'RAM
Heuris/c
Numerical Results
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4@layer'stream
QoS'level'4'
achieved'by'
33.1%'vs.'60%'
of'the'users'
QoS'level'4'
achieved'by'
65%'vs.'60%'
of'the'users'
SFN'BS
QoS'level'4
QoS'level'3
QoS'level'2
QoS'level'1

22. 4. Concluding Remarks
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23. Concluding Remarks
19
๏ We presented a viable method for the incorporation of the
UEP-NC scheme into LTE-A stack as a means of improving
the reliability of scalable video multicast services
๏ Inspired by a fundamental economics principle, we defined
of a novel resource allocation framework that aims to
improve the service coverage with a reduced resource
footprint.
๏ We defined a novel heuristic strategy that can efficiently
derive a good quality resource allocation solution of the
considered problem.
๏ Results showed that our modeling ensures a service coverage
which is up to 2.5-times greater than that of the considered
conventional MrT strategy.
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24. Thank you for
your attention
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For more information
http://goo.gl/Z4Y9YF
A. Tassi, I. Chatzigeorgiou, and D. Vukobratović, “Resource Allocation
Frameworks for Network-coded Layered Multimedia Multicast
Services”, IEEE J. Sel. Areas Commun., vol. 33, no. 2, Feb. 2015

25. London, 9th June 2015
R2D2
EPSRC First Grant
Scheme (EP/L006251/1)
Optimized Network-coded
Scalable Video Multicasting
over eMBMS Networks
IEEE ICC 2015 - Mobile and Wireless Networking Symposium
A. Tassi, I. Chatzigeorgiou, D. Vukobratović, A. L. Jones
a.tassi@{lancaster.ac.uk, bristol.ac.uk}
andCommunications
School of Computing