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Multicast Service Delivery Solutions in

LTE-Advanced Systems

Leonardo Militano, Massimo Condoluci, Giuseppe Araniti, Antonio IeraDIIES Dept., ARTS Lab., University Mediterranea of Reggio Calabria, Italy

e-mail:[leonardo.militano|massimo.condoluci|araniti|antonio.iera]@unirc.it

AbstractMulticast services over Long Term Evolution (LTE)have recently attracted the interest of the research commu-nity. Particular attention has been put on handling fairnessrequirements of multicast users and experienced channel quality.Conventional Multicast Scheme (CMS), Opportunistic MulticastScheme (OMS), and multicast subgroup formation are theprincipal policies adopted to address this research issue. Littleattention has been given so far to multicarrier systems like LTE-Advanced although the need for solutions appropriate to thissystem is strongly felt. In this paper we design radio resourcemanagement policies for the efficient delivery of multicast ser-

vices in multicarrier LTE-Advanced systems and show how therelation between fairness and system efficiency can be controlled.Concerning the system resource management, game theoreticbargaining solutions are considered to best model fairness andefficiency (expressed in terms of throughput).

Index TermsLTE-Advanced, Multicast, Efficient Scheduling,Radio Resource Management

I. INTRODUCTION

LONG Term Evolution (LTE) is probably the most promis-ing wireless system able to support group-oriented ser-vices (i.e., multicast and broadcast) over mobile devices [1].

An issue attracting several research activities is represented by

the effective handling of different fairness requirements and

diverse channel quality experienced by multicast users in the

same Multicast Group (MG). Different approaches for man-

aging multicast services have been proposed in the literature

[2] [3]. A first approach is the Conventional Multicast Scheme

(CMS) [4], where the total throughput is limited by the user

with the worst channel conditions. As a consequence, CMS

might be considered as as a fair policy, since all the users are

served at the same throughput. Nevertheless, it achieves a poor

performance in terms of network throughput and satisfaction

experienced by the users with good channel conditions. An

alternative approach is the Opportunistic Multicast Scheme

(OMS) [5]. According to it, not all User Equipments (UEs)

are served in a given time slot. This allows to maximize

the system throughput according to the channel quality. A

third alternative approach to overcome the limitations of CMS

and OMS algorithms is based on the multicast subgrouping

policy [6] [7], whereby multicast destinations are grouped into

different subgroups depending on the UE channel quality.

The research of Massimo Condoluci is supported by European Union,European Social Fund and Calabria Regional Government. This paper reflectsthe views only of the authors, and the EU, and the Calabria RegionalGoverment cannot be held responsible for any use which may be made ofthe information contained therein.

When considering multicast services, little attention has

been given so far to multicarrier systems like LTE-Advanced

[8]. The main objective of the present paper is to investigate

in this direction, propose possible solutions to adopt in the

multicarrier scenario, and compare the system performance in

terms of system efficiency (expressed by the total achievable

throughput) and fairness among the UEs (this is a well-known

analysis in wireless networks [9]). In our opinion blind

extensions of approaches studied for LTE standard are not the

best solution, as they do not exploit the enhanced potentials ofthe multiple carriers available. The conceptual ideas of CMS,

OMS and subgrouping solutions proposed for LTE can still

be valid in multicarrier systems. Notwithstanding, a further

investigation can lead to an improved service performance.

We propose a general framework featuring the possibility of

tuning some system settings so to meet the desired fairness and

efficiency properties. In particular, game theoretic bargaining

solutions are considered for the resource allocation in the

multicarrier system [10]-[11]. Besides, the possibility to have a

differentiated behaviour on the available carriers is introduced.

The performance of the proposed framework is compared to

alternative solutions: the mere extension of CMS and OMS

policies to the multicarrier system.

I I . RESEARCH BACKGROUND

The downlink LTE air interface is based on the Orthogonal

Frequency Division Multiple Access (OFDMA). The spectrum

is managed in terms of Resource Blocks (RBs): in the fre-

quency domain each RB corresponds to 12 consecutive and

equally spaced sub-carries. The RB is the smallest frequency

resource, which can be assigned to a UE. The overall number

of available RBs varies from 6 (1.4 MHz channel bandwidth)

to 100 (20 MHz) [7]. LTE is considered as the first step toward

the real 4G wireless system [12], under investigation by the

3GPP in the LTE-Advanced project [8]. The main objective

of this project, in terms of spectrum management, is to allowwider bandwidth up to 100 MHz while keeping backward

compatibility with LTE. In order to meet this expectation,

a carrier aggregation scheme is proposed which consists of

grouping up to five LTE Component Carriers (CCs, each one

up to 20 MHz). A Packet Scheduler (PS) is implemented at

the Medium Access Control (MAC) layer relevant to each CC

[13]. The main functionality of the PS is to efficiently handle

the resource allocation in the time and frequency domains.

The Frequency Domain Packet Scheduler (FDPS) relevant

978-1-4673-3122-7/13/$31.00 2013 IEEE

IEEE ICC 2013 - Wireless Networking Symposium

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to each CC is in charge of the spectrum management. The

FDPS assigns the adequate number of RBs to each scheduled

user and selects the modulation and coding scheme (MCS)

for each RB. These procedures are conducted based on the

Channel Quality Indicator (CQI) feedback transmitted by the

UE to the base station and are the main focus of the proposed

framework. The CQI is associated to the maximum supported

MCS [7]; Transmission parameters (i.e., MCS) are adapted

every CQI Feedback Cycle (CFC), which can last one or

several Transmission Time Interval (TTI, equal to 1 ms) [7].

Based on previous experiences on the LTE standard system,

we identify four different approaches that can be followed to

properly design a FDPS in multicarrier systems: 1) Multicar-

rier CMS (MC-CMS), where all UEs are served on every CC in

each TTI, according to the worst CQI among all UEs in every

CC; 2) Multicarrier OMS (MC-OMS), where not necessarily

all UEs have to be served by the system in a given TTI, and

the system throughput is maximized according to the CQI of

the UEs on every CC; 3)Conservative-Opportunistic Multicast

Scheme (COMS), where all UEs are served by the system in

each TTI, but not necessarily on every CC, the CMS solutionis adopted on one CC only, while the OMS solution is adopted

on the remaining CCs [14]; 4)Multicarrier Subgrouping (MS),

where all UEs are served by the system in each TTI, but not

necessarily on every CC, and multicast subgrouping solutions

are adopted with constraints on the per CC handling.

While the MC-CMS approach is a straightforward extension

of the CMS solution for LTE to all the available CCs, the

remaining approaches can be modeled by setting an appropri-

ate constraint on the different CCs in the subgroup formation

framework presented in the next section.

III. MULTICAST S UBGROUPF ORMATION ANDR ESOURCE

ALLOCATION

Let us consider a general case where the MG is composed

by NLTE-Advanced devices. Let c be the CQI value associ-

ated to a generic user, where c can vary from 1 to 15.1 For a

given CQI value c, the attainable throughput depends on the

number of assigned RBs, which can vary from 1 to R, whereR

depends on the system bandwidth configuration. The proposed

resource allocation and multicast subgroup formation algo-

rithm foresees the following steps: 1) CQI collection phase:

the eNodeB collects the CQI feedbacks from each UE on every

CC; 2) Definition of potential subgroup partitions per CC: by

accounting for the collected CQI feedbacks, it determines all

the possible combinations of MCSs that can be activated by

forming a correspondent number of multicast subgroups. The

basic assumption is that all the UEs experiencing the same CQI

value are associated to the same multicast subgroup. Of course,

a subgroup could serve UEs with different CQI values. With

this assumption, the possible number of multicast subgroups

to activate per CC varies from 1 to 15; 3) Radio resourceallocation phase: the resources to be allocated to any of the

1We assume no errors in the CQI estimation. The impact of the CQIestimation errors on the performance is shown in Section IV-A.

potential subgroup combinations are determined according to

an appropriate game theoretic bargaining solution; 4) Multicast

subgroups creation phase: according to a chosen performance

indexPand based on the resource allocations for the potential

multicast subgroup partitions, the final choice is made on

which of the multicast subgroups to activate in every CC.

As already mentioned, the Radio resource allocation phase

in this paper is based on game theoretic solutions, which

offer the possibility to nicely investigate on the existing

dualism between fairness and efficiency of the framework [15].

First, we need to define the utility of the involved multicast

subgroups. In particular, the total utility for a generic activated

subgroupj is strictly related to the number of associated UEs,

and the number of assigned RBs:

uj =Vj gj (1)

where Vj is the number of UEs associated to subgroup j and

gj represents the data rate assigned by the base station to

the j-th subgroup and varies as a function of the MCS and

the RBs (i.e., 1 RBj R) assigned to the j-th subgroup

[7]. The so defined utility is used for the Multicast subgroupscreation phase. In particular, for thePindex we will adopt the

Aggregate Utility (AU), that is the total utility of the system:

AU =J

j=1 uj , where J is the total number of enabled

subgroups for a candidate subgroups partition.

A. Bargaining Solutions for Radio Resource Allocation

For the Radio Resource Allocation Phase, four different

game theoretic bargaining solutions are considered in this

paper. For the axiomatic definition the interested reader can

refer to the literature, while here the basic formulations are

briefly reported. Let K={1, 2, . . . , K } be the set of playerswhich compete for the resources assignment. Each player

is characterized by a performance function fj defined on aconvex closed and non-empty set X RK, which is the set ofgame strategies for the Kplayers. The performance functions

define the performance vector f(x)=(f1(x),...,fK(x))). The set

F={u RK: x X, u=(f1(x),...,fK(x))} is the set ofachievable performance values (or utilities). Each player is

also characterized by an initial minimum performance, the

so-called disagreement point, the players require to enter the

game, d=(d1,d2,...,dK) RK, such that Fd={u F: u d}

= . Let Xd={x X: f(x) d} be the subset of strategiesthat enable the players to achieve at least their disagreement

point performance.

In the reference problem, the potential MCS levels to be

activated will be the players of the game, the performancefunctions fj are defined as in equation (1), while the setting

of the disagreement point d will be discussed later in this

section as this is the key parameter for the implementation of

the different solutions discussed in section II. As for the X

set for the bargaining problem, this is the set of possible RB

assignments to each player. This is actually not a convex set

in RK because only integer values of RB can be assigned to

each subgroup. Nevertheless, this issue is solved by studying

the problem on the smallest convex set containing X, the

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convex hull Xc (thus, also the utility set F becomes the

convex hull Fc). A final step is introduced to approximate

the bargaining solutions, defined on the convex hull set, to the

discrete integer-value domain of RBs and to the corresponding

utilities [16].

Nash Bargaining Solution (NBS): The idea promoted by

the NBS is that after a minimal requirement is satisfied for all

players, the remaining resources are allocated according to the

conditions of each player. As demonstrated in the literature, the

proportional fairness in scheduling problems is a special case

of the NBS, and it is equivalent to the following optimization:

N BS= M axjJ

ln(fj(x) dj),x Xd (2)

Kalai-Smorodinsky Solution (KSS): According to KSS,

the utilities assigned to the players should be proportional

to their maximum possible values. In practice, this solu-

tion assigns the maximal point of the feasible set on the

segment connecting the disagreement point to the so-called

utopia point. This ideal point, in which each player would

get his maximum possible benefit, is defined as hj(F, d) =max{uj |u F}, j K. For the reference problem, theutopia point is considered as the point where all available

RBs are assigned to the given MCS level. A formulation for

the KSS can be given in the form of a weighted max-min

fairness solution, which focuses on improving the payoff of the

weakest players, weighted according to the best-case payoff:

KS S= maxuF,

minjK

uj djhj dj

(3)

Egalitarian Solution (ES): The ES assigns the point in the

feasible set where all players achieve maximal equal increase

in utility w.r.t. the disagreement point. The formulation can begiven in the form of a strict max-min fairness:

ES= maxuF

minjK

(uj dj) (4)

Utilitarian Solution (US): The US maximizes the sum

of the utilities and is, therefore, also called as Maximum

throughput scheduler in scheduling problems:

U S= maxuF,ujdjj

Kj=1

uj (5)

B. Setting the disagreement point

An appropriate definition of the disagreement point in thegame theoretic solutions will set the constraints to model the

four different approaches listed in section II. In particular,

by adopting the US as bargaining solution and setting the

disagreement point equal to 0 in every CC, the MC-OMS

approach can be modeled. By applying the same reasoning

on only 4 CCs and adopting a CMS solution on the remaining

CC, the COMSapproach is obtained. Finally, the multicarrier

Subgroupingapproach can be modeled by setting the disagree-

ment point to one only in a subset of CCs and to zero in the

remaining CCs. In this case, the question arises on which of

the available CCs it is more convenient to set the disagreement

point equal to one. As already stated, all users are assumed

to be active on all carriers. Therefore, to increase the overall

system throughput performance, the CCs on which to set a

non-zero disagreement point are those with the highest average

in the CQI values for the UEs in the relative CCs.

According to the different considered approaches, each UE

will be associated to a variable number of CCs. Such a

system configuration can, for instance, be adopted for a content

distribution service based on Multiple Description Coding

(MDC) with different unique content descriptors being sent

to the activated multicast subgroups. A UE associated to more

multicast subgroups will receive a correspondent number of

descriptors increasing the quality of the received content.

IV. PERFORMANCEA NALYSIS

We conducted a simulation campaign where, according to

the LTE-Advanced assumptions, R = 100 RBs are availableon each of the five CCs. Channel conditions for each UE

are evaluated in terms of Signal to Interference and NoiseRatio (SINR) when path-loss, shadowing, and multipath fading

affect the signal reception [17]. The effective SINR, calculated

through the Exponential Effective SIR Mapping (EESM), is

eventually mapped onto the CQI level ensuring a BLER

smaller than 10% [17]. We consider a typical on-campus

scenario with N = 100 UEs forming the LTE-Advancedmulticast group distributed over a concentrated area within

the cell. We focus on the results achieved only in a single

TTI, to analyze the effectiveness of the proposed FDPSs.

Differences among the proposed approaches and bargaining

solutions are mainly based on network coverage plots (defined,

for a given throughput x, as the percentage of UEs served

with a throughput equal or lower than x), as this parameterbest shows the differences in terms of fairness and system

efficiency for the solutions.

For the sake of clearness in the presentation of the results,

we will first focus only on the MC-CMS, MC-OMS and

COMS solutions (see Fig. 1). As it clearly appears from the

plot, the MC-CMS and MC-OMS approaches show somehow

a dual behavior in terms of fairness and system efficiency. In

particular, the MC-CMS shows very low throughput values and

high fairness, while the MC-OMS shows higher throughput

and lower fairness. These results are in line with what obtained

in LTE standard study cases (see e.g., [10]), with the difference

of higher throughput values achieved in the LTE-Advanced

system since 5 CCs are now available. Another aspect tounderline is that the MC-OMS solution is not serving all

the UEs in the system in a given TTI, hence it requires

additional data coding for reliable traffic delivery to the

whole set of multicast members [5]. In particular, the results

showed that, on average, only 50% of the UEs are actually

served by the system, while in all other considered solutions

the system guarantees that all UEs are served at least in

one CC. When considering instead the COMS solution, the

performance is somehow a trade-off in terms of fairness and

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Fig. 1. Network coverage for CM-CMS, CM-OMS and COMS solutions.

efficiency between the previous two solutions. Nevertheless,

even if the advantages of the COMS solution are evident, still

enhancements are possible in the flexibility of the system.

By this we mean that by applying a simple solution, such as

the COMS, there is no possibility to tune the performance

levels according to our system and service requirements. Such

a flexibility can be obtained, instead, with the game theoretic

multicarrier subgrouping solutions.In analyzing the performance for the multicarrier subgroup-

ing solutions we consider, as a further variable, the number of

CCs on which we require that all UEs are served. As described

in section III-B, such a system choice can be modeled by

properly setting the disagreement point in the different CCs.

In particular, we consider three study cases: (A), Disagreement

point set to one on 1 CC and to zero on 4 CCs; (B),

Disagreement point set to one on 3 CC and to zero on 2 CCs;

(C), Disagreement point set to one on all 5 CCs.

As it clearly appears from the plots in Fig. 2, in all the

three cases the MS-ES shows a higher fairness and a lower

throughput; the MS-US offers instead a higher throughput

and a lower fairness; finally, the MS-KSS and MS-NBS are

falling in between the other two solutions. Really interesting

to observe is that in the three different study cases the

relation among the bargaining solutions remains the same;

the differences become more evident when increasing the

number of CCs where the disagreement point is set to one.

The reason for this result is that, in most of the cases, the

system will choose to work with a classic OMS approach on

the CCs where the disagreement point is set to zero (since no

constraints exist on the UEs to serve) and forms subgroups on

the other CCs. Thus the differences in the bargaining solutions

are more evident in study case C, Fig. 2(c).

To conclude the analysis, in Table I we summarize, for allthe considered solutions, the average throughput per node, the

average percentage number of UEs served in each CC, and the

average number of multicast subgroups formed in the system.

Moreover, the fairness index (FI), as defined in equation (6),

is also reported:

F I= (N

i=1 Ti)2

N(N

i=1 Ti2)

(6)

where Ti is the total throughput for UE i in the system (on

TABLE IPERFORMANCEF IGURES FOR D IFFERENT M ULTICARRIER A PPROACHES.

Case Throughput FI Served UEs Number of per CC Subgroups

MC-CMS 14.1 Mbps 1 100% 5

MC-OMS 67.1 Mbps 0.412 45.6% 5

COMS 56.2 Mbps 0.431 56.2% 5

MS-NBSA 60.9 Mbps 0.417 100% 10B 63.3 Mbps 0.432 78.1% 8

C 65.8 Mbps 0.445 56.3% 6

MS-KSSA 37.6 Mbps 0.418 100% 10B 49.3 Mbps 0.436 78.1% 8C 61.1 Mbps 0.468 56.3% 6

MS-ESA 23 Mbps 0.423 100% 13.2B 40.4 Mbps 0.453 78.1% 9.6C 57.9 Mbps 0.509 56.3% 6.4

MS-USA 66.5 Mbps 0.412 100% 10B 66.7 Mbps 0.412 78.1% 8C 66.9 Mbps 0.412 56.3% 6

all available CCs); FI = 1 is the maximum fairness, achieved

when all UEs are served with the same throughput as for

the MC-CMS solution. As it can be read from the Table,

the overall throughput obtained by each UE in the system

is the highest with the MC-OMS and the lowest with MC-

CMS. As for the bargaining based solutions, the throughput

per UE is the highest in case C and the lowest in case A.

Moreover, comparing these values to the COMS solution, in all

tested cases the MS-NBS and the MS-US solution outperform

the COMS, while for the MS-KSS and MS-ES this happens

only in case C. When looking instead at the FI, no important

difference can be observed between the MS-US and MC-OMS

solutions. The further three bargaining solutions are always

outperforming the COMS solution (cases B and C), with the

MS-ES showing the highest FI, followed by the MS-KSS and

the MS-NBS. For case A instead, the FI for these solutions islower than the COMS. Concerning the average percentage of

served UEs per CC, it is interesting to see that this coincides

for all subgrouping policies. This is an expected result since

for these solutions all UEs are served when the disagreement

point is set to one, while in the remaining CCs all solutions

actually have the same OMS behavior. Consequently, in these

cases, all UEs are served by the system, but not on every

CCs. Concerning the number of subgroups that are formed,

this number increases with the number of CCs where the

disagreement point is set to one. In particular, the highest

number of groups activated is obtained with the MS-ES in

Case A (i.e., 13.2).

A. Impact of the CQI estimation assumption

As briefly described in Section III, a simplification has

been made in the analysis as errors in the CQI estimation are

not considered. To justify the assumption, in this section the

impact that this has on the proposed solutions is evaluated,

and it is shown that it does not introduce major errors or

inefficiencies into the system. For this analysis, we approxi-

mated the measured SINR with the ideal value and an additive

independent identically distributed zero mean Gaussian error

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(a) Case A. (b) Case B. (c) Case C.

Fig. 2. Network coverage for Multicarrier Subgrouping solutions.

Fig. 3. ADR variation due to CQI estimation errors.

with standard deviation equal to 1 dB [18]. In Fig. 3, the

system ADR variation is plotted, by varying the percentage of

UEs reporting an imperfect CQI value. It can be observed that

in general the considered solutions are robust to such errors(as long as the standard deviation is below 2 dB). Even in

the worst case, when all the UEs reported an incorrect CQI,

the ADR variation is always lower than 10%. Moreover, weobserve that the ES is always less affected by the errors.

V. CONCLUSIONS

This paper presented an analysis of solutions for an efficient

delivery of multicast services in LTE-Advanced networks.

Extensions of classic solutions adopted for the LTE standard,

like CMS and OMS, have been investigated for the multicarrier

system. It has been shown that the main advantages and

drawbacks of the two approaches are even more pronounced

in LTE-Advanced systems where up to five CCs are available.A mixed solution, considering CMS on one CC and OMS

on the remaining CCs, proved a better trade-off performance.

As an alternative, multicast subgrouping policies have been

considered in the LTE-Advanced system within a framework

based on cooperative bargaining solutions.We showed how it

is possible to tune the system performance and the relationship

between fairness and efficiency, by setting differentiated bar-

gaining constraints on the available CCs. Finally, an analysis

on the impact of errors in the CQI estimation has been

presented, showing that this impact is negligible in general.

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