Presentation of 'Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks'. IEEE ICC 2015, London, UK

1. London, 11th June 2015
Sleep Period Optimization Model For
Layered Video Service Delivery Over
eMBMS Networks
IEEE ICC 2015 - SAC, Energy Efficient Wireless Systems
Lorenzo Carlà, Francesco Chiti, Romano Fantacci, A. Tassi
a.tassi@{lancaster.ac.uk, bristol.ac.uk}

2. Starting Point and Goals
๏ Delivery of multimedia broadcast/multicast services over
4G/5G networks is a challenging task. Especially for the point
of view of the user battery efficiency.
๏ During the reception of high data rate video streams, the user
radio interface is in an active state for a not be negligible time.
That has an impact on the battery, we minimize the active time.
๏ There are many studies dealing with DRX optimization but they
mainly refer to Point-to-Point services.
Goals
๏ Advanced eMBMS Scenario - Scalable video service (such as
H.264/SVC) multicasting.
๏ Resource optimisation - Minimizing the transmission time of
a video stream and, hence, the battery footprint.
2

3. Index
1. System Parameters and Performance Analysis
2. Proposed Resource Allocation Modeling and
Heuristic Solution
3. Analytical Results
4. Concluding Remarks
3

4. 1. System Parameters and Performance Analysis

5. System Model
๏ One-hop wireless communication system composed of a Single
Frequency Network (SFN) and a multicast group of U users
5
๏ Layered service consists of a basic layer and multiple
enhancement layers.
๏ All the BSs forming the SFN multicast the same layered video
service, in a synchronous fashion.
๏ Reliability ensured via the RLNC principle.
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 performed over each service layer independently
from the others.
๏ The source node will linearly combine the data packets
composing the -th layer and will generate a stream of
coded packets , where
๏ is a layered source message of source
packets, classified into service layers
x = {x1, . . . , xK}
RLNC Principle
yj =
klX
i=1
gj,i xi Coef%icients+of+the+linear+
combination+are+selected+a+
certain+%inite+%ield
K1 K2 K3
x1 x2 xK. . .. . .
K
L
KL
`
{yj}j

7. 7
RLNC and LTE-A
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 Depending+on+the+
MCS+a+certain+no.+of+
cod.+el.+are+mapped+
onto+a+PDU+
๏ Coded elements of different layers cannot be mixed within a PDU
๏ One PDU per PHY layer Transport Block. TBs of the same layer are
transmitted with the same power.

8. N`(P`, t`) '
$
ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `

9. N`(P`, t`) '
$
ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
Tx+pow.

10. N`(P`, t`) '
$
ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
Tx+pow. No.+of+PDU+tx

11. N`(P`, t`) '
$
ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
Tx+pow. No.+of+PDU+tx
Cod.+el.+bit+length
TTI+durationUser+reception+rate

12. N`(P`, t`) '
$
ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
u `๏ A user recovers the layer if it collects linearly
independent coded elements associated with that layer, which
occurs with probability
✴ 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
gu(P`, t`) =
K` 1Y
j=0

1
1
qN`(P`,t`) j
K`
Tx+pow. No.+of+PDU+tx
Cod.+el.+bit+length
TTI+durationUser+reception+rate

13. 2. Proposed Resource Allocation Modelling and
Heuristic Solution

14. Problem Formulation
10
๏ The battery efficiency is obtained by accommodating the
transmission power and the number of PDU transmissions
per service layer.
(MSP) min max
`2{1,...,L}
t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U ` 2 {1, . . . , L} (2)
K`  t`  dGoP ` 2 {1, . . . , L} (3)
LX
`=1
P`  ˆP (4)
P` 2 R+
, t` 2 N ` 2 {1, . . . , L} (5)
During+each+subframe+the+total+
transmission+power+is+limited+
Each+service+level+shall+be+achieved+
by+a+predetermined+fraction+of+users+
within+a+certain+time.+
Max.+Sleep+Period

15. Problem Heuristic
๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the
following heuristic strategy.
11
(MSP) min max
`2{1,...,L}
t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U ` 2 {1, . . . , L} (2)
K`  t`  dGoP ` 2 {1, . . . , L} (3)
LX
`=1
P`  ˆP (4)
P` 2 R+
, t` 2 N ` 2 {1, . . . , L} (5)

16. Problem Heuristic
๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the
following heuristic strategy.
11
(MSP) min max
`2{1,...,L}
t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U ` 2 {1, . . . , L} (2)
K`  t`  dGoP ` 2 {1, . . . , L} (3)
LX
`=1
P`  ˆP (4)
P` 2 R+
, t` 2 N ` 2 {1, . . . , L} (5)
(USP)
Unconst.+Sleep+Period

17. Problem Heuristic
๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the
following heuristic strategy.
12
(USP-`) min t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U (2)
K`  t`  dGoP (3)
๏ Proposition - If the solution of (USP-l) exists, it belongs to
๏ However, the USP solution may not be feasible for MSP.
L`
.
=
n
(P`, t`) 2 R+
⇥ N K`  t`  dGoP ^
PU
u=1
⇣
gu(P`, t`)
⌘
ˆ)= ˆ✓`U
o

18. Problem Heuristic
๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the
following heuristic strategy.
12
(USP-`) min t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U (2)
K`  t`  dGoP (3)
๏ Proposition - If the solution of (USP-l) exists, it belongs to
๏ However, the USP solution may not be feasible for MSP.
L`
.
=
n
(P`, t`) 2 R+
⇥ N K`  t`  dGoP ^
PU
u=1
⇣
gu(P`, t`)
⌘
ˆ)= ˆ✓`U
o

19. Problem Heuristic
๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the
following heuristic strategy.
12
(USP-`) min t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U (2)
K`  t`  dGoP (3)
๏ Proposition - If the solution of (USP-l) exists, it belongs to
๏ However, the USP solution may not be feasible for MSP.
L`
.
=
n
(P`, t`) 2 R+
⇥ N K`  t`  dGoP ^
PU
u=1
⇣
gu(P`, t`)
⌘
ˆ)= ˆ✓`U
o
t`
P`

20. Problem Heuristic
๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the
following heuristic strategy.
12
(USP-`) min t` (1)
subject to
UX
u=1
⇣
gu(P`, t`) ˆ
⌘
ˆ✓`U (2)
K`  t`  dGoP (3)
๏ Proposition - If the solution of (USP-l) exists, it belongs to
๏ However, the USP solution may not be feasible for MSP.
L`
.
=
n
(P`, t`) 2 R+
⇥ N K`  t`  dGoP ^
PU
u=1
⇣
gu(P`, t`)
⌘
ˆ)= ˆ✓`U
o
t`
P`

21. Heuristic Procedure
13
t`
P`
dGoP
๏ H-MSP makes feasible the optimum solution of USP.
Frontier+of USP-1
USP-2
Frontier+of USP-3
Frontier+of

22. Heuristic Procedure
13
t`
P`
dGoP
๏ H-MSP makes feasible the optimum solution of USP.
Optimum+values+of+++++++++++++++++,+++++++++++++++++and+
K1 K2 K3
USP-1 USP-2 USP-3
Frontier+of USP-1
USP-2
Frontier+of USP-3
Frontier+of

23. Heuristic Procedure
13
t`
P`
dGoP
๏ H-MSP makes feasible the optimum solution of USP.
(i) Start+from+the+UTP+solution+
(ii) Alter+the+component+with+the+greatest+value+of++
(iii) Repeat+the+proc.+until+the+power+budget+is+met.
t`
Optimum+values+of+++++++++++++++++,+++++++++++++++++and+
K1 K2 K3
USP-1 USP-2 USP-3
Frontier+of USP-1
USP-2
Frontier+of USP-3
Frontier+of

24. 3. Numerical Results

25. Numerical Results
15
๏ We compared the proposed strategies with a classic Uniform
Power Allocation (UPA) strategy
๏ System performance was evaluated in terms of
Relies+on+the+considered+
LTELA+stack
(UPA) min
`2{1,...,L}
t`
subject to K`  t`  dGoP ` 2 {1, . . . , L}
P` = ˆP/L ` 2 {1, . . . , L}
✏=
dGoP max
`=1,...,L
(t`)
dGoP
Normalized+sleep+period

26. Numerical Results
16
SFN cell sector
Interfering cell sector
SFN base station
Interfering base station
Center of the Cell I
Center of the Cell II
Scenario+with+a+high+
heterogeneity.+80+UEs+
equally+spaced
We+considered+
3Llayer+and+4Llayer+
streams

27. Numerical Results
17
20 25 30 35 40 45 50 55 60 65 70 75 80
0
0.2
0.4
0.6
0.8
Power [W]
ε
MSP
H-MSP
UPA
6 RBP
9 RBP
12 RBP
3Llayer+stream
MSP+and+HLMSP+are+close
(PDUs+of+6+RBPs)
1+RBP+=+180+KHz

28. Numerical Results
17
20 25 30 35 40 45 50 55 60 65 70 75 80
0
0.2
0.4
0.6
0.8
Power [W]
ε
MSP
H-MSP
UPA
6 RBP
9 RBP
12 RBP
3Llayer+stream
MSP+and+HLMSP+are+close
(PDUs+of+6+RBPs)
1+RBP+=+180+KHz

29. Numerical Results
17
20 25 30 35 40 45 50 55 60 65 70 75 80
0
0.2
0.4
0.6
0.8
Power [W]
ε
MSP
H-MSP
UPA
6 RBP
9 RBP
12 RBP
3Llayer+stream
MSP+and+HLMSP+are+close
(PDUs+of+6+RBPs)
UPA+cannot+always+%ind+a+solution
(PDUs+of+6+RBPs)
1+RBP+=+180+KHz

30. Numerical Results
18
20 25 30 35 40 45 50 55 60 65 70 75 80
0
0.2
0.4
0.6
0.8
Power [W]
ε
MSP
H-MSP
UPA
6 RBP
9 RBP
12 RBP
MSP+and+HLMSP+are+close
(PDUs+of+6+RBPs)
UPA+cannot+always+%ind+a+solution
(PDUs+of+6+RBPs)
1+RBP+=+180+KHz
4Llayer+stream

31. 4. Concluding Remarks

32. Concluding Remarks
20
๏ We propose an optimal and heuristic radio resource
allocation strategy, namely MSP and H-MSP strategies,
which maximize the user sleep period and improve the
reliability of communications by means of an optimized
RLNC approach
๏ Not only the the user energy consumption is reduced but
also the developed strategies can meet the desired QoS
levels
๏ Results show that the developed H-MSP strategy provide a
good quality feasible solution to the MSP model in a finite
number of steps
๏ The proposed strategy is characterized by sleep periods that
are up to 40% greater than those provided by the considered
UPA approach.

33. Thank you for
your attention
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

34. London, 11th June 2015
Sleep Period Optimization Model For
Layered Video Service Delivery Over
eMBMS Networks
IEEE ICC 2015 - SAC, Energy Efficient Wireless Systems
Lorenzo Carlà, Francesco Chiti, Romano Fantacci, A. Tassi
a.tassi@{lancaster.ac.uk, bristol.ac.uk}