presentation of 'reliable rate-optimized video multicasting services over lte/lte-a

Rel iab le Rate-Opt imized Video Mul t icast ing Serv ices over

LTE/LTE-AAndrea Tass i

F rancesco Ch i t iRomano Fan tacc i

Chad i Kh i ra l l ahJohn Thompson

De jan Vukobra tov ić

C o n t a c t s{name . su r name}@un i f i . i t{name . su r name}@ed .ac .uk{name . su r name}@kt io s . ne t

Index1. Novel multi-rate design for eMBMS

LTE/LTE-A networks based on RNC

2. B a c k g r o u n d and previous works

3. RNC-based rate allocation model f o r SC-eMBMS systems

4. N u m e r i c a l r e s u l t s

1. Novel Multi-Rate Design for eMBMS LTE/LTE-A Networks Based on RNC

Rate-Optimized Multicast ServicesConsidering LTE/LTE-A systems:

๏ Frame composed by RBs, TTI/2 x 12 subcarriers

๏ Data organized in TBs of a variable number of RBPs(1 RBP = 1 TTI x 12 subcarriers)

TB

RBP

TTI

RB

Rate-Optimized Multicast ServicesConsidering LTE/LTE-A systems:

๏ Frame composed by RBs, TTI/2 x 12 subcarriers

๏ Data organized in TBs of a variable number of RBPs(1 RBP = 1 TTI x 12 subcarriers)

LTE (from 3GPP’s Rel. 9) can manage broadcast and multicast services by the eMBMS framework.

The proposed design provides:

๏ Reliability, based on RNC transmission

๏ Efficiency, obtained by optimized rate allocation across multi-rate video streams.

TB

RBP

TTI

RB

2. Background and Previous Works

Previous Works (1/2)

Problem of rate adaptation in multicast communication systems has been addressed as follows:

๏ Cell-Edge Strategies - Rate allocation satisfies a set of QoS constraints across all UEs, including the cell-edge UEs

๏ Least Channel Gain Strategies - As the CE-S but it matches the QoS constraints of UEs in the worst propagation conditions (at the moment)

๏ Multi-Rate Adaptation Strategies - Several streams of different quality are optimized. Hence, They can manage heterogenous channel conditions.

Proposed RAM strategy belongs to the latter family.

๏ Suitable for transmitting H.264 AVC multicast video services

๏ Each flow is directed to a specific MG

๏ UEs in the worst channel conditions have to decode at least the lowest channel rate sub-stream

๏ UEs in a better channel conditions can decode the higher data rate sub-streams to yield high QoS results.

Previous Works (2/2)

๏ Coded symbols are transmitted until the MG receive a set of K linearly independent coded symbols.

Reliable Rate-Optimized Video MulticastingServices over LTE/LTE-A

Andrea Tassi⇤, Chadi Khirallah†, Dejan Vukobratovic‡, Francesco Chiti⇤, John Thompson†, Romano Fantacci⇤⇤Department of Electronics and Telecommunications, University of Florence, Florence, Italy

†School of Engineering, The University of Edinburgh, Edinburgh, UK‡Dept. of Power, Electronics and Communication Engineering, University of Novi Sad, Serbia

Abstract—In this paper, we propose a novel advanced multi-

rate design for evolved Multimedia Multicast/Broadcast Ser-

vice (eMBMS) in fourth generation (4G) Long-Term Evolution

(LTE)/LTE-Advanced (LTE-A) networks. The proposed design

provides: i) reliability, based on random network coded (RNC)

transmission, and ii) efficiency, obtained by optimized rate allo-

cation across multi-rate RNC streams. The paper provides an in-

depth description of the system realization and demonstrates the

feasibility of the proposed eMBMS design using both analytical

and simulation results. The system performance is compared with

popular multi-rate multicast approaches in a realistic simulated

LTE/LTE-A environment.

I. INTRODUCTION

ci =

KX

j=1

gj xj

Video content delivery over fourth generation (4G) net-works, such as Long-Term Evolution (LTE)/LTE-Advanced(LTE-A), is estimated to grow 18-fold between 2011-2016[?]. This growth is mainly attributed to the surge in demandfor bandwidth-intensive mobile multimedia services by LTE-based smartphones, tablets and laptops. 3GPP standards pro-vide a bandwidth efficient broadcast and multicast solutionthat is suitable for the simultaneous delivery of multimediacontent to large groups of users; namely, the MultimediaBroadcast/Multicast Service (MBMS) [?].

Current MBMS services use the Unacknowledged Modeat the radio link control (UM RLC) layer for data transmis-sion due to the lack of ability to process acknowledgementmessages from users (UEs). Thus, MBMS avoids the use ofcomplex automatic repeat request (ARQ) schemes that canresult in significant packet delays caused by the retransmissionprocesses that are used to recover lost packets. Nonethe-less, in order to ensure reliable data delivery in multicastnetworks, in the case of delay-sensitive applications, 3GPPstandards have integrated application-layer forward error-correction (AL-FEC) solutions. In particular, Raptor codes [?]are included in the MBMS standard to provide protectionagainst channel erasures.

Recently, the proliferation of packet-level network codingtechniques has increased interest in application-layer randomnetwork coding (AL-RNC) as a viable AL-FEC solution.However, AL-FEC leads to a large amount of redundancy(delays) on the end-to-end links, compared to the short real-time multimedia message transmission time. They also requireadditional resources over the entire data path, including a

reliable over-provisioned core network infrastructure. For thesereasons, in [?], the integration of RNC solutions at the mediumaccess control (MAC) layer of the LTE/LTE-A radio accessnetwork (RAN), is proposed to exploit the very small MACround-trip delay, in addition to a number of MAC-RNCbenefits specific to media streaming services [?].

The focus of the paper is to investigate a novel advancedmulti-rate design for the MBMS service. The proposed ap-proach is based on the base station (eNB) delivering multipleMAC-RNC protected multicast streams matched to the setof heterogeneous user classes in the cell. The core of theproposed solution is an optimized rate-allocation strategyapplied to share the available bandwidth resources amongdifferent multi-rate RNC streams. The paper demonstrates thefeasibility of the proposed RNC-based rate-optimized MBMSservice through analytical and numerical simulation results.Its efficiency is demonstrated by evaluating its performanceagainst the most popular multi-rate multicast approaches withrealistic LTE/LTE-A parameter settings.

The paper is organized as follows. Section II providesthe necessary background on the existing MBMS service inLTE/LTE-A and an overview of the MAC-RNC proposal forLTE/LTE-A. In Section III, we present the proposed multi-rate MBMS solution. Section IV provides both analytical andsimulation results. Finally, conclusions to the paper are givenin Section V.

II. BACKGROUND

A. Multimedia Multicasting Services in LTE/LTE-A

3GPP standards provided several designs for MBMS start-ing from Release 6, which allowed point-to-multipoint trans-mission schemes [?]. In Release 8 [?], the enhanced MBMS(eMBMS) design is introduced with two proposed transmis-sion schemes. The single-cell MBMS mode (SC-eMBMS)overcomes previous shortcomings by allowing user feedbackon channel conditions and dynamic selection of the suitablemodulation and coding (MC) mode. The multi-cell, or socalled single frequency network MBMS mode (SFN-eMBMS),provides a coordinated effort of several eNBs to cover thenetwork with the same physical signal. In this mode, afixed MC scheme is adapted to match the edge-of-cell userrequirements. In this paper, we focus on SC-eMBMS in orderto exploit the dynamic data rate adaptation to different usersin the cell [?].

UE1UE2

UE3UE4

UEYeNB

Multicast3Group

RNC for Reliable MulticastingCommunication reliability of a videosub-flow has been improved by using the RNC as (a kind of) FEC:

๏ MG of Y UEs

๏ Each information message is K symbols long

๏ They are linearly combined to generate coded symbols

3. RNC-based Rate Allocation Model for SC-eMBMS systems

SC-RNC-eMBMS Stack

๏ M multicast groups๏ CQI of a MG is the worst-case

one๏ IP data streams are segmented/

concatenated into RLC PDUs๏ RLC PDU is split in K

information symbols๏ # of cod. symbols per TB

TABLE IIEXAMPLE SET OF MULTICAST GROUPS.

MG Index Spanned Rings

1 r12, r13, r14, r152 r10, r113 r6, r7, r8, r94 r4, r5

The PHY TB transmission model for each MG requires: i)modeling the reported channel quality indicator (CQI) valuesas seen by the UEs, and ii) estimating PHY TB error rates fordifferent CQI values as seen from the MAC layer. We modelthe reported CQI values and their dynamics using Finite-StateMarkov Chain (FSMC) channel models [?] as described indetail in [?]. In short, based on the 3GPP defined macro-cellular path-loss model [?], we calculate the average SINRat the UE location, which is passed to the FSMC modelwhose states are aligned with SINR intervals correspondingto different CQI values.

We introduce a sequence of NCQI = 16 concentric circlesdefined by the set of radii r = {r0 = 0, r1, . . . , rNCQI}, whereri is the distance between the eNB and the external edge of thei-th ring (ri�1, ri). The i-th ring defines an area within whicha UE can receive PHY TBs with error probability less thana threshold value Pe, given that the eNB transmits using thei-th MC scheme (that corresponds to CQI=i). The set r can beobtained from the steady-state probabilities of the CQI-stateFSMC model described in [?]; however, the details are omittedfor brevity. Note that if a MG uses the i-th MC scheme, theUEs within the circle of radius ri will receive the stream ofPHY TBs with reconstruction probability of at least Pe. Thusthe selection of the number of MGs M and the correspondingMC schemes can be mapped to a partition of r, as shown inTable II (M = 4, MC={12,10,6,4}). In this paper we assumeda partition scheme such that the area of the circle of radius di

(where di is the distance between the eNB and the outermostring belonging to the i-th MG) increases close to linearly.

The capacity of PHY TBs broadcasted over the k-th MG de-pends on the MC mode selected and the amount of frequencyresources allocated to the k-th MG:

Cy = ny ·NRBP,y · L [bits] (1)

where nk and NRBP,k represent the number of coded symbolsper PHY TB (as reported in Table I) and the frequency allo-cation (expressed in terms of number of RBPs), respectively.For LTE/LTE-A system parameters, we adopt the source/codedsymbol length (in bits) of L = 32 [?]. Thus a single PHYTB on the k-th MG holds exactly F = nk · NRBP,k codedsymbols. It is important to note that all the UEs belonging tothe same MG will receive PHY TBs allocated over the sameset of resource block paris (RBPs). Hence, in the rest of thispaper, we use NRBP,k to refer to the frequency allocation tothe k-th MG.

Finally, we assume that a set of UEs are placed in the macro-cell area of radius Rc using a Poisson point process of averagedensity �. The hit probability that a UE is placed at the i-thring defined by ri�1 r ri is ph,i = (r

2i � r

2i�1)/(R

2c).

The average number of UEs per ring is ui = � · ph,i.

B. Combinatorial Error Model for Multicast Communications

Let Vi (for i = 1, . . . , uk) be a random variable representingthe number of PHY TB transmissions performed by the eNBto ensure the correct reception of a RLC PDU by the i-thUE in the k-th MG. In other words, Vi is the number oftransmission attempts to ensure the correct reception of K

linearly independent coded symbols by the i-th UE. Also, let

U = max

i=1,...,uk

n

Vi

o

(2)

be a random variable representing the number of PHY TBtransmissions performed by the eNB to ensure the correctreception of the RLC PDU by all receiving UEs [?].

Let pi be the probability of reception of a PHY TB at the i-th UE. The cumulative density function (CDF) of Vi for j � 0

and j · nk ·NRBP,k � K is expressed as (see Eq. (12) of [?])

Pr{Vi j} = fi(j) =

jX

a=1

ui

�

a, j

�

w

�

ank NRBP,k

�

(3)

where (see Eqs. (12) and (7) of [?])

ui(n, c) =

8

<

:

✓

c

n

◆

p

ni

h

1� pi

ic�nif K n c

0 otherwise,(4)

and,

w(c) =

8

>

<

>

:

K�1Y

b=0

h

1� 1

q

c�b

i

if c � K

0 otherwise.(5)

For j · nk ·NRBP,i < K we have that fi(j) = 0.From (2) and (3) the expected value of the random variable

U can be expressed as [?]:

E[U ] = ⇣(uk, NRBP,k) =

=

1X

n=0

n

(

Prn

U n

o

� Prn

U n� 1

o

)

=

=

1X

n=0

n

(

ukY

r=1

fr(n)�ukY

r=1

fr(n� 1)

)

. (6)

The expression (6) is validated using numerical simulationsfor a network scenario composed of one MG that contains 10UEs. Each UE receives PHY TBs with a delivery probabilityof pi = 0.89. The RLC PDU length K = 100, the PHY TBcapacity (in terms of coded symbols) is equal to 10, 50 or 100and, the finite field size is q 2 [2

2, 2

8] interval. The results are

averaged over 103 simulation runs.Fig. 2 shows the expected value U as a function of the

generation length (K) for different PHY TB sizes (TBS). FromFig. 2, we observe that with the increase in the PHY TBS,the value of U becomes less sensitive to the increase in thelength of the RLC PDU. Fig. 2 confirms the match betweenthe proposed analytical model and the simulation results.

# cod. symbols per RBP Depends on CQI of MG

# RBPs per TB

PDCP$PDU PDCP$PDU

RLC$PDU

x1 x2 xK

⊗ g2⊗ g1 ⊗ gK

⊕

c1 c2 ciMAC$PDU

TB

PDCP

RLC

MAC

MAC(RNC

PHY

NRBP,y

Combinatorial Error Model (1/2)

๏ Characterisation of the r.v. Vi. The i-th UE recovers a RLC PDU after j TBs with a prob. equal to

Delivery prob. of a over j TBs Prob. that at least K symbols are lin. indep. over a finite field Fq



1 r12, r13, r14, r152 r10, r113 r6, r7, r8, r94 r4, r5








2i � r

2i�1)/(R

2c).





U = max

i=1,...,uk

n

Vi

o

(2)




Pr{Vi j} = fi(j) =

jX

a=1

ui

�

a, j

�

w

�

any NRBP,y

�

(3)


ui(n, c) =

8

<

:

✓

c

n

◆

p

ni

h

1� pi

ic�nif K n c

0 otherwise,(4)

and,

w(c) =

8

>

<

>

:

K�1Y

b=0

h

1� 1

q

c�b

i

if c � K

0 otherwise.(5)



E[U ] = ⇣(uk, NRBP,k) =

=

1X

n=0

n

(

Prn

U n

o

� Prn

U n� 1

o

)

=

=

1X

n=0

n

(

ukY

r=1

fr(n)�ukY

r=1

fr(n� 1)

)

. (6)


2, 2




if K ≤ a ≤ j, otherwise it is 0



1 r12, r13, r14, r152 r10, r113 r6, r7, r8, r94 r4, r5

detail in [?]. In short, based on the 3GPP defined macro-cellular path-loss model [?], we calculate the average SINRat the UE location, which is passed to the FSMC modelwhose states are aligned with SINR intervals correspondingto different CQI values.




Ck = nk ·NRBP,k · L [bits] (1)



2i � r

2i�1)/(R

2c).



Let Vi (for i = 1, . . . , uk) be a random variable representingthe number of PHY TB transmissions performed by the eNBto ensure the correct reception of a RLC PDU by the i-thUE in the k-th MG. In other words, Vi is the number of

20 30 40 50 60 70 80 900

2

4

6

8

10

12

14

Generation Length ( K )

E[U

]

F = 10, Theor.F = 10, Sim.F = 50, Theor.F = 50, Sim.F = 100, Theor.F = 100, Sim.

Fig. 2. The expected number of transmission attempts value as function ofthe generation length (K), with q = 22 and uk = 10 for different TB sizes.

transmission attempts to ensure the correct reception of K


U = max

i=1,...,uk

n

Vi

o

(2)




Pr{Vi j} = fi(j) =

jX

a=1

ui

�

a, j

�

w

�

ank NRBP,k

�

(3)


ui(a, j) =

✓

j

a

◆

p

ai

h

1� pi

ij�a(4)

ui(a, j) =

8

>

>

<

>

>

:

✓

j

a

◆

p

ai

h

1� pi

ij�aif K a j

0 otherwise

(5)

and,

w(c) =

K�1Y

b=0

h

1� 1

q

c�b

i

(6)

w(c) =

8

>

<

>

:

K�1Y

b=0

h

1� 1

q

c�b

i

if c � K

0 otherwise.(7)


U can be expressed as [?]: The expression (7) is validatedusing numerical simulations for a network scenario composedof one MG that contains 10 UEs. Each UE receives PHY TBswith a delivery probability of pi = 0.89. The RLC PDU lengthK = 100, the PHY TB capacity (in terms of coded symbols)is equal to 10, 50 or 100 and, the finite field size is q 2 [2

2, 2

8]

interval. The results are averaged over 103 simulation runs.Fig. 2 shows the expected value U as a function of the

generation length (K) for different PHY TB sizes (TBS). From

if c ≥ K, otherwise it is 0



1 r12, r13, r14, r152 r10, r113 r6, r7, r8, r94 r4, r5








2i � r

2i�1)/(R

2c).





U = max

i=1,...,Y

n

Vi

o

(2)




fi(j) =

jX

a=1

ui

�

a, j

�

w

�

any NRBP,y

�

(3)


ui(n, c) =

8

<

:

✓

c

n

◆

p

ni

h

1� pi

ic�nif K n c

0 otherwise,(4)

and,

w(c) =

8

>

<

>

:

K�1Y

b=0

h

1� 1

q

c�b

i

if c � K

0 otherwise.(5)



E[U ] = ⇣(uk, NRBP,k) =

=

1X

n=0

n

(

Prn

U n

o

� Prn

U n� 1

o

)

=

=

1X

n=0

n

(

ukY

r=1

fr(n)�ukY

r=1

fr(n� 1)

)

. (6)


2, 2




๏ The r.v. representing the number of PHY TB transmissions to ensure the reception of the RLC PDU by the MG is



1 CQI12 - CQI152 CQI10 - CQI113 CQI6 - CQI94 CQI4 - CQI5

conditions are assigned higher rate MC schemes in order toincrease their PHY layer transmission rates; while an MG inpoorer channel conditions need to receive PHY TBs processedusing a low rate MC scheme to be able to decode those TBssuccessfully. We assume that only a single MAC PDU can beplaced within a PHY TB, and one PHY TB can be transmittedto a MG during a given transmission time interval (TTI).






where nk and NRBP,k represent the number of coded symbolsper PHY TB (as reported in Table I) and the frequency allo-cation (expressed in terms of number of RBPs), respectively.For LTE/LTE-A system parameters, we adopt the source/codedsymbol length (in bits) of L = 32 [?]. Thus a single PHYTB on the k-th MG holds exactly F = nk · NRBP,k codedsymbols. It is important to note that all the UEs belonging tothe same MG will receive PHY TBs allocated over the sameset of resource block paris (RBPs). Hence, in the rest of this

paper, we use NRBP,k to refer to the frequency allocation tothe k-th MG.


2i � r

2i�1)/(R

2c).

The average number of UEs per ring is ui = � · ph,i.B. Combinatorial Error Model for Multicast Communications



U = max

i=1,...,uk

n

Vi

o

(7)




Pr{Vi j} = fi(j) =

jX

a=1

ui

�

a, j

�

w

�

ank NRBP,k

�

(8)


ui(a, j) =

✓

j

a

◆

P

j�ae

h

1� Pe

ia(9)

ui(a, j) =

8

>

>

<

>

>

:

✓

j

a

◆

p

ae

h

1� pe

ij�aif K a j

0 otherwise

(10)

and,

w(c) =

K�1Y

b=0

h

1� 1

q

c�b

i

(11)

w(c) =

8

>

<

>

:

K�1Y

b=0

h

1� 1

q

c�b

i

if c � K

0 otherwise.(12)



2, 2

8]




๏ The avg. number of U is

Avg. value of U

⇣(Y,NRBP,y) =

1X

n=0

n

"

YY

i=1

fi(n)�YY

i=1

fi(n� 1)

#

. (13)

20 30 40 50 60 70 80 900

2

4

6

8

10

12

14


E[U

]



C. Rate-Allocation Optimisation

In the proposed SC-RNC-eMBMS solution, the set of M

MGs share the fixed frequency resource allocation of ˆ

NRBP

RBPs reserved for eMBMS. In this section, we formulatethe rate-allocation optimization that targets optimal frequencyresource sharing among MGs with the goal of matching thesustainable data rates requested by the set of heterogeneousUEs. The presented Rate Adaptation Model (RAM) fits to thesystem model where the optimal rate allocation is provided bythe eNB, for example, for maximum user satisfaction duringthe transmission of layered scalable video source. We assumethat the provided allocation (i.e., the dimensions in terms ofRBPs of PHY TBs directed to each MG) cannot be changedduring the video broadcasting process.

From Eq. (13) the k-th MG will receive an informationstream characterized by a bit rate defined as follows:

by = by

⇣

Y,NRBP,y

⌘

=

L ·KTTI · ⇣(Y,NRBP,y)

[bps]. (14)

In this specific context the RAM optimisation model can bedefined as follows (N+

= N \ {0}):

minimizeMX

k=1

�k

⇣

1� CQIk

⌘

(15)

s.t. bk

⇣

uk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (16)

�k vk, k = 1, . . . ,M (17)MX

k=1

NRBP,k ˆ

NRBP (18)

NRBP,k 2 N+, k = 1, . . . ,M (19)

where the CQIk parameter represents the lowest CQI indexreported by the UEs belonging to the k-th MG normalized tothe maximum index that can be reported by an UE (i.e., 15).Parameter vk (for k = 1, . . . ,M ) represents the minimumsustained bit rate provided by the QoS profile characterizing

TABLE IIIMINIMUM SUSTAINED BITRATE MATCHED BY THE FA-S AND

LCG-S/RAM-S.

Generation FR-S Bitrate LCG-S/RAM-S Bitrate

Length [Mbps] [Mbps]

125 1.4 v1 = 4, v2 = 4, v3 = 4, v4 = 4625 1.5 v1 = 10, v2 = 6.7, v3 = 5, v4 = 41125 1.6 v1 = 9, v2 = 7.2, v3 = 6, v4 = 41200 1.6 v1 = 9.6, v2 = 7.7, v3 = 6.4, v4 = 3.81625 1.6 v1 = 10.4, v2 = 7.4, v3 = 5.8, v4 = 42125 1.6 v1 = 9.7, v2 = 7.5, v3 = 6.2, v4 = 4

the communication flow directed to the k-th MG. Finally, afeasible solution to the RAM optimization problem should beable to match all the minimum sustained bit rates character-izing each communication flow. However, it may happen thatthe number of RBPs required by some MGs is too large sothat the constraint (18) cannot be met. For this reason obviousQoS constraints such as:

bk

⇣

uk, NRBP,k

⌘

� vk, k = 1, . . . ,M (20)

have to be relaxed and restated as expressed by the constraints(16). Overall, the optimization objective is that violations tothe QoS constraints are minimized (i.e., Eq. (15) minimizesthe weighted sum of the overall system violations to the QoS).The RAM optimization problem has been solved by resortingto suitable derivative free methods [?], [?].

IV. ANALYTICAL AND SIMULATION RESULTS

The performance of the allocation strategy defined by theRAM optimization model (the RAM-S strategy) has beencompared to two widely used rate allocation strategies [?]:

• The Fixed Rate Strategy (FR-S) - the eNB transmits onlyone downlink flow with a bitrate that matches the QoSrequirements of the UE in the outermost cell-ring. Theminimum sustained bitrates (vk) assigned to cell-edgeUEs are given in the second column of Table III;

• The Least Channel Gain Strategy (LCG-S) - the eNBbroadcasts only a traffic flow with a bitrate that matchesthe vh value (where h is the non-empty MG characterizedby the lowest CQI index). The third column of Table IIIgives the considered minimum sustained bitrates (vk).

The parameters vk (for k = 1, . . . ,M = 4) of the RAM-Sare provided in the third column of Table III. Finally, in thefollowing, we considered network scenarios characterized byan eNB inter-site distance (ISD) of 1000m.

Let bk be the average downlink bitrate characterizing thedownlink flow directed to the k-th MG (defined by Eq. (14)).The average cell rate ˆ

b can be defined as follows:

ˆ

b =

MX

l=1

bl [bps]. (21)

p.m.f. of U


๏ The avg. information bit rate of the MG is

E[U ] = ⇣(Y,NRBP,y) =

1X

n=0

n

(

Prn

U n

o

� Prn

U n� 1

o

)

=

1X

n=0

n

(

YY

r=1

fr(n)�YY

r=1

fr(n� 1)

)

. (6)




NRBP



by = by

⇣

Y,NRBP,y

⌘

=


[bps]. (7)


= N \ {0}):

minimizeMX

k=1

�k

⇣

1� CQIk

⌘

(8)

s.t. bk

⇣

uk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (9)

�k vk, k = 1, . . . ,M (10)MX

k=1

NRBP,k ˆ

NRBP (11)

NRBP,k 2 N+, k = 1, . . . ,M (12)

where the CQIk parameter represents the lowest CQI indexreported by the UEs belonging to the k-th MG normalized tothe maximum index that can be reported by an UE (i.e., 15).Parameter vk (for k = 1, . . . ,M ) represents the minimumsustained bit rate provided by the QoS profile characterizingthe communication flow directed to the k-th MG. Finally, afeasible solution to the RAM optimization problem should beable to match all the minimum sustained bit rates character-izing each communication flow. However, it may happen thatthe number of RBPs required by some MGs is too large sothat the constraint (11) cannot be met. For this reason obviousQoS constraints such as:

bk

⇣

uk, NRBP,k

⌘

� vk, k = 1, . . . ,M (13)

have to be relaxed and restated as expressed by the constraints(9). Overall, the optimization objective is that violations tothe QoS constraints are minimized (i.e., Eq. (8) minimizes theweighted sum of the overall system violations to the QoS).The RAM optimization problem has been solved by resortingto suitable derivative free methods [?], [?].


LCG-S/RAM-S.











ˆ

b =

MX

k=1

bk [bps]. (14)

Hence, ˆb represents the actual bitrate that the UEs belongingto the innermost MG will be able to get by combining eachmulticast flow broadcasted by the eNB (see Sec. III-A).

Moreover, let ok = bk/vk be the gain index characterizingthe k-th MG. If ok � 1 then the QoS constraint of the dataflow is guaranteed. The average cell gain index is defined as:

o =

PMk=1 | uk>0 CQIk okPM

k=1 | uk>0 CQIk

. (15)

It represents an aggregated measure of the capability of a givenrate allocation strategy to match the required QoS constraints.

Fig. 3 shows the average cell rate as function of K forthe LCG-S, FR-S and the proposed RAM-S rate allocationstrategies for q = 2

4. From Fig. 3 we note that the performanceof the three strategies is not sensitive to the increase in the

The information bits belonging to the RLC PDU

1 ms

๏ The avg. number of U is

Avg. value of U

⇣(Y,NRBP,y) =

1X

n=0

n

"

YY

i=1

fi(n)�YY

i=1

fi(n� 1)

#

. (13)

20 30 40 50 60 70 80 900

2

4

6

8

10

12

14


E[U

]






NRBP



by = by

⇣

Y,NRBP,y

⌘

=


[bps]. (14)


= N \ {0}):

minimizeMX

k=1

�k

⇣

1� CQIk

⌘

(15)

s.t. bk

⇣

uk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (16)

�k vk, k = 1, . . . ,M (17)MX

k=1

NRBP,k ˆ

NRBP (18)

NRBP,k 2 N+, k = 1, . . . ,M (19)



LCG-S/RAM-S.





bk

⇣

uk, NRBP,k

⌘

� vk, k = 1, . . . ,M (20)









ˆ

b =

MX

l=1

bl [bps]. (21)

p.m.f. of U

Rate-Allocation Model (1/2)

๏ Cell area is split in 15 concentric rings, one per CQI

๏ i-th ring approximates an area within which UEs report the i-th CQI index (delivery prob. of a TB is Pe)

Considered scenario

* MG receives just one MBMS flow. Layered video flow (such as H.264 AVC)

MG1 MG2



1 CQI12 - CQI152 CQI10 - CQI113 CQI6 - CQI94 CQI4 - CQI5

conditions are assigned higher rate MC schemes in order toincrease their PHY layer transmission rates; while an MG inpoorer channel conditions need to receive PHY TBs processedusing a low rate MC scheme to be able to decode those TBssuccessfully. We assume that only a single MAC PDU can beplaced within a PHY TB, and one PHY TB can be transmittedto a MG during a given transmission time interval (TTI).






where nk and NRBP,k represent the number of coded symbolsper PHY TB (as reported in Table I) and the frequency allo-cation (expressed in terms of number of RBPs), respectively.For LTE/LTE-A system parameters, we adopt the source/codedsymbol length (in bits) of L = 32 [?]. Thus a single PHYTB on the k-th MG holds exactly F = nk · NRBP,k codedsymbols. It is important to note that all the UEs belonging tothe same MG will receive PHY TBs allocated over the sameset of resource block paris (RBPs). Hence, in the rest of this

paper, we use NRBP,k to refer to the frequency allocation tothe k-th MG.


2i � r

2i�1)/(R

2c).

The average number of UEs per ring is ui = � · ph,i.B. Combinatorial Error Model for Multicast Communications



U = max

i=1,...,uk

n

Vi

o

(7)




Pr{Vi j} = fi(j) =

jX

a=1

ui

�

a, j

�

w

�

ank NRBP,k

�

(8)


ui(a, j) =

✓

j

a

◆

P

ae

h

1� Pe

ij�a(9)

ui(a, j) =

8

>

>

<

>

>

:

✓

j

a

◆

p

ae

h

1� pe

ij�aif K a j

0 otherwise

(10)

and,

w(c) =

K�1Y

b=0

h

1� 1

q

c�b

i

(11)

w(c) =

8

>

<

>

:

K�1Y

b=0

h

1� 1

q

c�b

i

if c � K

0 otherwise.(12)



2, 2

8]


















I. INTRODUCTION

minimizeMX

k=1

�k

⇣

15� CQIk

⌘

(1)

s.t. bk

⇣

Yk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (2)

�k vk, k = 1, . . . ,M (3)MX

k=1

NRBP,k ˆ

NRBP (4)

NRBP,k 2 N+, k = 1, . . . ,M (5)


Current MBMS services use the Unacknowledged Modeat the radio link control (UM RLC) layer for data transmis-sion due to the lack of ability to process acknowledgementmessages from users (UEs). Thus, MBMS avoids the use ofcomplex automatic repeat request (ARQ) schemes that canresult in significant packet delays caused by the retransmissionprocesses that are used to recover lost packets. Nonethe-less, in order to ensure reliable data delivery in multicastnetworks, in the case of delay-sensitive applications, 3GPPstandards have integrated application-layer forward error-correction (AL-FEC) solutions. In particular, Raptor codes [?]

are included in the MBMS standard to provide protectionagainst channel erasures.

Recently, the proliferation of packet-level network codingtechniques has increased interest in application-layer randomnetwork coding (AL-RNC) as a viable AL-FEC solution.However, AL-FEC leads to a large amount of redundancy(delays) on the end-to-end links, compared to the short real-time multimedia message transmission time. They also requireadditional resources over the entire data path, including areliable over-provisioned core network infrastructure. For thesereasons, in [?], the integration of RNC solutions at the mediumaccess control (MAC) layer of the LTE/LTE-A radio accessnetwork (RAN), is proposed to exploit the very small MACround-trip delay, in addition to a number of MAC-RNCbenefits specific to media streaming services [?].



II. BACKGROUND


3GPP standards provided several designs for MBMS start-ing from Release 6, which allowed point-to-multipoint trans-mission schemes [?]. In Release 8 [?], the enhanced MBMS(eMBMS) design is introduced with two proposed transmis-sion schemes. The single-cell MBMS mode (SC-eMBMS)overcomes previous shortcomings by allowing user feedbackon channel conditions and dynamic selection of the suitablemodulation and coding (MC) mode. The multi-cell, or so


(4) TB size optimization (i.e., NRBP,k optimization for each flow) matching the system constraints.
















I. INTRODUCTION

minimizeMX

k=1

�k

⇣

15� CQIk

⌘

(1)

s.t. bk

⇣

Yk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (2)

�k vk, k = 1, . . . ,M (3)MX

k=1

NRBP,k ˆ

NRBP (4)

NRBP,k 2 N+, k = 1, . . . ,M (5)







II. BACKGROUND




(2)-(3) vk is the information bit rate per MG of the k-th QoS profile. If it cannot be matched, the slack variable δk is not null.

















I. INTRODUCTION

minimizeMX

k=1

�k

⇣

15� CQIk

⌘

(1)

s.t. bk

⇣

Yk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (2)

�k vk, k = 1, . . . ,M (3)MX

k=1

NRBP,k ˆ

NRBP (4)

NRBP,k 2 N+, k = 1, . . . ,M (5)







II. BACKGROUND




(2)-(3) vk is the information bit rate per MG of the k-th QoS profile. If it cannot be matched, the slack variable δk is not null.


(1) the O.F. minimizes the weighted sum of the violations.

4. Numerical Results

๏ SC-eMBMS LTE-A deployment (20 MHz of bandwidth)

๏ H.264 AVC-like video flow of 4 layers (i.e., M = 4)

๏ K = 1200 is the same for each video layer

Considered Scenarios

We compared the RAM strategy with:

๏ CE-S – resources have been optimized w.r.t. the cell edge

๏ LCG-S – the optimization takes place by considering the worst UEs

๏ SC-eMBMS LTE-A deployment (20 MHz of bandwidth)

๏ H.264 AVC-like video flow of 4 layers (i.e., M = 4)

๏ K = 1200 is the same for each video layer

Considered Scenarios

We compared the RAM strategy with:

๏ CE-S – resources have been optimized w.r.t. the cell edge

๏ LCG-S – the optimization takes place by considering the worst UEs

Avg cell rate

E[U ] = ⇣(Y,NRBP,y) =

1X

n=0

n

(

Prn

U n

o

� Prn

U n� 1

o

)

=

1X

n=0

n

(

YY

r=1

fr(n)�YY

r=1

fr(n� 1)

)

. (13)

20 30 40 50 60 70 80 900

2

4

6

8

10

12

14


E[U

]






NRBP



by = by

⇣

Y,NRBP,y

⌘

=


[bps]. (14)


= N \ {0}):

minimizeMX

k=1

�k

⇣

1� CQIk

⌘

(15)

s.t. bk

⇣

uk, NRBP,k

⌘

� vk � �k, k = 1, . . . ,M (16)

�k vk, k = 1, . . . ,M (17)MX

k=1

NRBP,k ˆ

NRBP (18)

NRBP,k 2 N+, k = 1, . . . ,M (19)



LCG-S/RAM-S.





bk

⇣

uk, NRBP,k

⌘

� vk, k = 1, . . . ,M (20)









ˆ

b =

MX

l=1

bl [bps]. (21)

Information bitrate

125 625 1125 1625 21250

3

6

9

12

15


Ave

rage

Cel

l Rat

e [M

bps]

FR−SLCG−SRAM−S

Fig. 3. The average cell rate as function of the K for different rate allocationstrategies and considering � = 15 UEs in the cell.

Hence, ˆb represents the actual bitrate that the UEs belongingto the innermost MG will be able to get by combining eachmulticast flow broadcasted by the eNB (see Sec. III-A).

Moreover, let ok = bk/vk be the gain index characterizingthe k-th MG. If ok � 1 then the QoS constraint of the dataflow is guaranteed. The average cell gain index is defined as:

o =

PMl=1 |Yl>0 CQIl olPM

l=1 |Yl>0 CQIl

. (22)

It represents an aggregated measure of the capability of a givenrate allocation strategy to match the required QoS constraints.

Fig. 3 shows the average cell rate as function of K forthe LCG-S, FR-S and the proposed RAM-S rate allocationstrategies for q = 2

4. From Fig. 3 we note that the performanceof the three strategies is not sensitive to the increase in thePHY TB size F (see Sec. III-B). The same observations areobtained from Fig. 4 which demonstrates the average cellrate as a function of the field size q, assuming K = 1125

RNC symbols. Finally, Fig. 3 and Fig. 4 show a significantperformance gain of almost 6-fold for the proposed RAM-Smethod compared to the LCG-S and FR-S schemes.

Fig. 5 and 6 show the average cell rate and the averagegain index as a function of � for different q and K = 1200

RNC symbols. Both the proposed RAM-S and the LCG-Sschemes demonstrate reduced average cell rate and gain indexwith increased �. On the other hand, the FR-S performanceis not sensitive to the increase in the user density. Overallthe proposed RAM-S rate allocation scheme significantlyoutperforms both the LCG-S and FR-S schemes.

V. CONCLUSIONS

In this paper we address the challenge of optimal rateallocation across multi-rate users in LTE/LTE-A networks, byusing a novel combination of the MAC-layer RNC and theSC-MBMS approach. Our optimization and analytical model isconfirmed by the simulation results which demonstrate signifi-cant throughput gains of 4-6 fold in average cell rate comparedto the currently proposed multi-rate allocation schemes forLTE/LTE-A systems.

2 8 16 32 64 128 2560

3

6

9

12

15

Finite Field Size ( q )

Ave

rage

Cel

l Rat

e [M

bps]

FR−SLCG−SRAM−S

Fig. 4. The average cell rate as function of the finite field size (q) for differentrate allocation strategies and considering � = 15 UEs in the cell.

2.5 5 7.5 10 12.5 150

5

10

15

20

25

30

User Density ( h )

Ave

rage

Cel

l Rat

e [M

bps]

FR−S (q = 22)LCG−S (q = 22)RAM−S (q = 22)FR−S (q = 28)LCG−S (q = 28)RAM−S (q = 28)

Fig. 5. The average cell rate as function of the Poisson � value, results areprovided for different rate allocation strategies.

2.5 5 7.5 10 12.5 150

0.5

1

1.5

2

User Density ( h )

Ave

rage

Gai

n In

dex

FR−S (22)LCG−S (22)RAM−S (22)FR−S (28)LCG−S (28)RAM−S (28)

Fig. 6. The average cell gain index as function of the Poisson � value, resultsare provided for different rate allocation strategies.

Avg gain index

ol = bl/vl

K = 1200 and q = 28

Avg Cell Rate

5 7.5 10 12.5 150

5

10

15

20

25

30

User Density ( h )

Ave

rage

Cel

l Rat

e [M

bps]

CE−S (q = 28)LCG−S (q = 28)RAM−S (q = 28)

K = 1200 and q = 28

Avg Cell Gain

5 7.5 10 12.5 150

0.5

1

1.5

2

User Density ( h )

Ave

rage

Gai

n In

dex

CE−S (28)LCG−S (28)RAM−S (28)

๏ Optimal rate allocation across multi-rate users in SC-eMBMS networks

๏ MAC-RNC sublayer is a self-contained solution which is useful to embed RNC within the LTE/LTE-A stack

๏ Results demonstrate significant throughput gains of 4-‐6 fold in average cell rate compared to the currently proposed multi-rate allocation schemes.

Conclusions and Future Works

Future works๏ Relate MGs to H.264 SVC video layers๏ Providing service-dependent rule to derive K๏ Providing efficient heuristic for allocation model

Thank you for your attention

Rel iab le Rate-Opt imized Video Mul t icast ing Serv ices over

LTE/LTE-AAndrea Tass i

F rancesco Ch i t iRomano Fan tacc i

Chad i Kh i ra l l ahJohn Thompson

De jan Vukobra tov ić

C o n t a c t s{name . su r name}@un i f i . i t{name . su r name}@ed .ac .uk{name . su r name}@kt io s . ne t

presentation of 'reliable rate-optimized video multicasting services over lte/lte-a

Engineering

optimized rate allocation

multirate video streams

novel multirate design

multirate rnc streams

problem of rate adaptation

embms ltelte

optimized rate allocation

higher data rate substreams