sleep period optimization model for layered video service delivery over embms networks
TRANSCRIPT
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}
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
Index
1. System Parameters and Performance Analysis
2. Proposed Resource Allocation Modeling and Heuristic Solution
3. Analytical Results
4. Concluding Remarks
3
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.
BSBS
BSBS
M1/M2
(MCE / MBMS-GW)
SFN
41
23
UE3UEUUE 2
UE1UE4
LTE-A Core Network
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 layersx = {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. . .. . .
KL
KL`
{yj}j
7
RLNC and LTE-AData$stream
associated$with$$ $
⊗⊗ ⊗⊕
TB
MAC
PHY
Data$streamassociated$with$$ $
MAC$PDUassociatedwith
x1 x2 xK. . .
gj,1 gj,2 gj,K2
. . .xK2
y1 yj. . . . . .
Source$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.
N`(P`, t`) '$ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
N`(P`, t`) '$ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
Tx+pow.
N`(P`, t`) '$ru(P`)
t` tTTI
L`
%
Performance Model
8
๏ User collects coded elements associated with layerN`u `
Tx+pow. No.+of+PDU+tx
N`(P`, t`) '$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
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
Problem Formulation
10
๏ The battery efficiency is obtained by accommodating the transmission power and the number of PDU transmissions per service layer.
(MSP) minmax
`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
Problem Heurist ic๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the following heuristic strategy.
11
(MSP) minmax
`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)
Problem Heurist ic๏ The MSP is an hard integer optimisation problem because of
the coupling constraints among variables. We proposed the following heuristic strategy.
11
(MSP) minmax
`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
Problem Heurist ic๏ 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
^P
U
u=1 �⇣
gu
(P`
, t`
)⌘
� �̂)= ✓̂`
Uo
Problem Heurist ic๏ 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
^P
U
u=1 �⇣
gu
(P`
, t`
)⌘
� �̂)= ✓̂`
Uo
Problem Heurist ic๏ 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
^P
U
u=1 �⇣
gu
(P`
, t`
)⌘
� �̂)= ✓̂`
Uo
t`
P`
Problem Heurist ic๏ 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
^P
U
u=1 �⇣
gu
(P`
, t`
)⌘
� �̂)= ✓̂`
Uo
t`
P`
Heurist ic Procedure
13
t`
P`
dGoP
๏ H-MSP makes feasible the optimum solution of USP.
Frontier+of USP-1USP-2
Frontier+of USP-3Frontier+of
Heurist ic 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-1USP-2
Frontier+of USP-3Frontier+of
Heurist ic 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-1USP-2
Frontier+of USP-3Frontier+of
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` (1)
subject to K` t` dGoP
` 2 {1, . . . , L} (2)
P` =ˆP/L ` 2 {1, . . . , L} (3)
✏=dGoP
� max
`=1,...,L(t`)
dGoP
Normalized+sleep+period
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
Numerical Results
17
20 25 30 35 40 45 50 55 60 65 70 75 800
0.2
0.4
0.6
0.8
Power [W]
ε
MSPH-MSPUPA6 RBP9 RBP12 RBP
3Llayer+stream
MSP+and+HLMSP+are+close(PDUs+of+6+RBPs)
1+RBP+=+180+KHz
Numerical Results
17
20 25 30 35 40 45 50 55 60 65 70 75 800
0.2
0.4
0.6
0.8
Power [W]
ε
MSPH-MSPUPA6 RBP9 RBP12 RBP
3Llayer+stream
MSP+and+HLMSP+are+close(PDUs+of+6+RBPs)
1+RBP+=+180+KHz
Numerical Results
17
20 25 30 35 40 45 50 55 60 65 70 75 800
0.2
0.4
0.6
0.8
Power [W]
ε
MSPH-MSPUPA6 RBP9 RBP12 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
Numerical Results
18
20 25 30 35 40 45 50 55 60 65 70 75 800
0.2
0.4
0.6
0.8
Power [W]
ε
MSPH-MSPUPA6 RBP9 RBP12 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
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.
Thank you for your attention
For more informationhttp://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
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}