IEEE TRANSACTIONS ON BROADCASTING 1 A Hybrid Unicast ... · 5G systems [5] have been considered as possible enabling approaches to efﬁciently manage the trafﬁc load and provide

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IEEE TRANSACTIONS ON BROADCASTING 1

A Hybrid Unicast-Multicast Network Selectionfor Video Deliveries in Dense Heterogeneous

Network EnvironmentsGiuseppe Araniti, Senior Member, IEEE, Pasquale Scopelliti, Student Member, IEEE,

Gabriel-Miro Muntean , Senior Member, IEEE, and Antonio Iera, Senior Member, IEEE

Abstract—Resource management in emerging dense heteroge-neous network environments (DenseNets) is a challenging issue.The employment of multicast transmissions in this scenario haspotential to address the problems. On one hand, the large numberof smart user mobile devices and user expectations for high-quality rich media services has determined a growing demandfor network resources; in DenseNets, mobile users have to makethe choice in terms of the network to connect to, in order tobalance energy saving and delivery performance. On the otherhand, the proliferation of user accesses to the existing and futurenetwork infrastructure will bring along with it the operators needfor optimizing the radio resource usage. This paper proposesa hybrid unicast-multicast utility-based network selection algo-rithm (HUMANS), which offers the additional option of selectingmulticast transmissions in the network selection process duringvideo delivery. By serving users with good channel conditionsvia unicast transmissions and users with poor channel qualityconditions via multicast, HUMANS allows outperforming othersolutions in terms of outage percentage and average qualityof transmission, in both low- and high-density scenarios. Mostimportantly, at the same time it guarantees operators a moreefficient resource utilization.

Index Terms—Network selection, video delivery, DenseNets,energy saving, multicasting, resource allocation.

I. INTRODUCTION

THE GROWING number of smart user mobile devices andthe raising demand for video-centric applications (e.g.,

video on demand, video games, live video streaming, videoconferencing, video surveillance, etc.) accessed via existingnetwork infrastructure make the provision of services at highquality very challenging.

Yet, providing these services at high quality and at low costin the emerging fifth generation network realm is fundamentalfor its market success. For instance, LTE-A [1] helps to over-come challenges related to edge-cell users and coverage holes.

Manuscript received November 27, 2017; revised February 22, 2018;accepted March 24, 2018. (Corresponding author: Gabriel-Miro Muntean.)

G. Araniti, P. Scopelliti, and A. Iera are with the DIIES Department,University Mediterranea of Reggio Calabria, 89100 Reggio Calabria, Italy(e-mail: [email protected]; [email protected]; [email protected]).

G.-M. Muntean is with the Performance Engineering Laboratory andSchool of Electronic Engineering, Dublin City University, 9 Dublin, Ireland(e-mail: [email protected]).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TBC.2018.2822873

The deployment of several femtocells within a macrocell areaserved by a Base Station (BS) can provide an improved cover-age (either indoor or in the coverage holes, for example) andan increase in the system capacity through the offloading ofsome of the macrocell’s traffic. Furthermore, edge-cell usersconnected to a femtocell should benefit from a higher datarate, low latency, and improved levels of Quality of Service(QoS) and Quality of Experience (QoE).

In the view of an increased capacity and improvedperformance of the system, recent researches push towardsthe deployment, within the same area, of several coveragelayers (associated to macro, micro, pico, and femto cells),diverse Radio Access Technologies (RAT) (e.g., GSM, UMTS,LTE, WiFi), and multiple Point-to-Point (PtP) user links(e.g., Device-to-Device communication [2], mmWave). Thismassive growth of dissimilar cell deployments is leadingto a high densification of networks and to the creation ofthe so-called Dense Heterogeneous Network (DenseNet) [3]paradigm.

Moreover, radio resources management (RRM) is stressedby the huge number of smart devices requiring video ser-vices. In this scenario, device-to-device (D2D) communica-tions [4] and multicast services over current LTE and future5G systems [5] have been considered as possible enablingapproaches to efficiently manage the traffic load and provide abetter Quality of Experience (QoE) to end-users. In particular,multicasting allows a large number of users to be simultane-ously served with relatively low latency and high throughput.To support such services, the Third Generation PartnershipProject (3GPP) offers basic support to the standardizationof multicast services over LTE under the name of enhancedMultimedia Broadcast Multicast Services (eMBMS) [6].

One of the most important issues for multicast transmissionis the management of multi-user diversity. In fact, each userwithin a multicast group experiences a different channel qual-ity level. Least channel gain users affect the performance ofthe whole multicast group as they can only support a trans-mission with low Modulation and Coding Scheme (MCS)level, thus achieving transmissions with bad spectral effi-ciency. On the contrary, serving multicast users that experiencehigh channel quality levels improves the system spectral effi-ciency, at the expense of users under bad channel conditions.This introduces challenging issues for the RRM in multicasttransmissions.

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https://orcid.org/0000-0002-9332-4770


2 IEEE TRANSACTIONS ON BROADCASTING

Our research focuses on a DenseNet deployment sce-nario characterized by overlapping of an LTE-A macro celland LTE-A small cells (i.e., femtocells), in the presence ofmulticast groups in each cell. In this scenario, mobile userswant to access video content at high user QoE levels and witha low energy consumption. Indeed, energy/power managementas well as user mobility management are key challenges in thenext generation mobile multimedia networks [7]. InnovativeRAT selection [8] solutions help in managing energy issues forsmart users in mobility. However, mobile users have to face theissue of the wise selection of the access network to connectto, which is made more challenging by the highly dynamicnetwork environment [9]. In particular, in a DenseNet context,network selection should place great importance to improvingthe balance between QoE of the video service offered to theuser and energy saving [10]. Furthermore, a proper manage-ment of network selection is needed, in order to avoid issuessuch as frequent and unnecessary handovers (i.e., the so-calledping-pong effect).

Access network selection schemes can be user-centric ornetwork-driven. In the user-centric approaches, the focus ison maximizing user QoE levels. However, this presents sev-eral limitations as users are only aware of their link quality,and have no information about the network load, which couldaffect user perceived quality and induce instability due to fre-quent handovers. In the network-driven solutions, the objectiveis to maximize the network operator revenues and maintainhigh overall user satisfaction by avoiding network congestionand by selecting the optimum interface for each user.

In this context, there is a need for a resource alloca-tion mechanism to provide the best available performanceto the largest number of users possible. The presence of amulticast transmission helps to obtain such a requirement, butat the cost of a compromise in terms of data-rate achievedby users within the multicast group. Generally, the methodol-ogy of resource allocation is to model it as an optimizationproblem whose objective function and constraints are deter-mined by user requirements and network specifications. Theobjective function is usually referred to as utility function,which characterizes a user satisfaction when allocated givenresources [11].

This paper proposes the Hybrid Unicast-Multicast utilitybased Network Selection algorithm (HUMANS), a networkselection approach that exploits the benefits of multicast duringvideo delivery. In such an approach both bandwidth utilization(an operator priority) and the trade-off between quality andenergy consumption (a user priority) are considered in decid-ing how to deliver video content in a DenseNet. HUMANS, intaking access-related choices, considers the estimated energyconsumption of the mobile device running a real-time videoapplication, the estimated achievable data-rate, the utilizedresources and the expected user satisfaction level.

A major contribution is, thus, a mechanism allowing for awise network selection choice, also considering a multicastgroup joining option, which at the same time meets usersexigencies and enables a smart bandwidth management.

The remainder of this paper is organized as follows. InSection II the major literature proposals related to our research

work are discussed from the perspectives of network selectionand RRM algorithms for multicast transmission. The referencesystem model is described in Section III, whereas the proposedHUMANS and its related utility function are presented inSection IV. Performance evaluation is performed and analysedin Section V, whereas conclusive remarks are summarized inSection VI.

II. RELATED WORKS

On the one hand, the 5G DenseNet environment will pro-vide increasing coverage and system capacity with respectto the current cellular networks. On the other hand, theDenseNet’s associated higher complexity exacerbates prob-lems of interference coordination, power consumption, RRMand mobility management. In such a DenseNet scenario, thereis a need for proper Network selection and resource allocationin order to meet both 5G requirements and user and marketexpectations in terms of, high QoE levels, increased power sav-ing, reduced cost, etc. State-of-the art related to our researchis discussed next, from the perspectives of network selectionsolutions and RRM algorithms in multicast transmissions.

A hybrid multimedia delivery solution which balances thebenefits of multimedia content adaptation and network selec-tion in order to decrease power consumption in a heteroge-neous wireless network environment, composed of UMTS andWLAN networks, was proposed in [10]. The trade-off betweenenergy and quality has been considered via a utility function.Similar approaches have been introduced in [12] and [13].Anedda et al. [12] propose an adaptive real-time Multi-useraccess network selection load balancing algorithm, taking intoaccount not only the real-time global traffic load on eachnetwork, but also considering the different classes of traf-fic. Whereas, the solution proposed in [13] combines severalinputs such as power of the received signal, throughput, packetdelay, cost-per-user, the requested type of traffic, and type ofdevice.

Raschellá et al. [14] propose a network selection solu-tion based on a novel algorithm, which relies on the conceptof Fittingness Factor (FF). The novel solution maximizes afunction that reflects the suitability of the available spectrumresources to the application requirements. The selection is car-ried out by taking into account specific parameters and QoSmetrics. The suitability of a network is determined by usingthe data bit rate required by the new flow and the bit rate thatthe network can support.

Several other network selection criteria have been presentedin literature, such as for instance the multi-criteria decisionmaking (MCDM). Trestian et al. [15] present performanceevaluation of a number of widely used multi-attribute decisionmaking solutions. TOPSIS [16] method has been exploitedin [17], where account user preferences, network conditions,QoS and energy consumption requirements have been takeninto account, in order to select the optimal network whichachieves the best balance between performance and energyconsumption. In [18] and [19] Game theory has been usedto perform network selection. The goal of [18] is maximizeaccommodated number of calls, minimize handoff occurrences


ARANITI et al.: HUMANS FOR VIDEO DELIVERIES IN DenseNets 3

frequency, and fulfill QoS requirements. Hence, the networkthat maximizes the utility value obtained through the gameis selected as the most suitable network for the call request.Game theory is exploited in user-centric manner in [19]. Thegame-theoretic approach carried out the negotiation betweenusers and network operators in terms of offered prices andservice quality.

Furthermore, in a DenseNet scenario with several small cellsdeployed, users are moving near the small cells and enter andexit in/from their coverage area with high frequency. Thisintroduces additional issues such as unnecessary handoverswith consequent reductions in terms of user QoE and systemcapacity. Orsino et al. [20] propose a RAT selection algo-rithm that efficiently manages the RAT handover procedure by(i) choosing the most suitable RAT that guarantees high systemand user performance, and (ii) reducing unnecessary handoverevents. They introduce a parameter named Reference BaseStation Efficiency that considers the BS transmitted power, BStraffic load and user spectral efficiency.

A different approach to avoid unnecessary handovers is auser mobility-aware technique that takes into account users’speed [21], [22]. Zhang et al. [21] proposed a handover algo-rithm based on the user speed and QoS. The authors suggestedthat users with high speed do not need to handover, as theycross the coverage area fast and especially when avail fromnon-real-time services, as this is inefficient. Nevertheless, theydid not consider any energy saving issue. An energy efficienthandover algorithm is proposed in [22] with the aim to reducepower consumption and frequent and unnecessary handovers.Users’ speed is accounted for in order to allow only slowusers performing handover. On the other hand, power savingis accomplished by decreasing the femtocell power transmis-sion in particular conditions. However, the energy managementproposed in [22] is network-side only, and does not considermobile device power consumption, a key aspect for users.

EMANS [23], instead, proposes an energy-saving networkselection algorithm, which provides a good trade-off betweenenergy consumption and perceived quality when deliveringvideo content. EMANS includes a method to adapt the deliv-ered video stream bitrate according to the available networkresources such as maintaining good user perceived quality lev-els. Furthermore, it also reduces the number of handovers incomparison with other state-of-the-art approaches.

All the works presented above deals with unicast trans-mission. Nevertheless, a dense 5G scenario should take intoaccount also group-oriented transmissions. In such a solution,the selection of the most proper MCS with which serve allusers is a challenging issue. A typical solution is representedby the conventional multicast scheme (CMS) where all theusers within a multicast group are served with the lowest levelMCS, representing users with worst channel condition [24].

The opportunistic multicasting [25] has been proposed inliterature as a possible solution to overcome the typical limi-tations of the conservative approach and to efficiently exploitmulti-user diversity, thus providing a more effective selectionof the MCS based on the users channel information. CMS andOMS are both single-rate transmission modes, where the BStransmits to all users in each multicast group at the same rate.

In Multi-rate, instead, the BS transmits to each user at differ-ent rates exploiting users frequency diversity, according to theheterogeneity of wireless channel.

The work presented in [26] optimally forms multicastgroups, based on the users data rate. Whereas,Borgiattino et al. [27] propose an approach for Single-Frequency Networks aiming to increase the aggregate datarateof the multicast group by pushing out of the transmission badchannel users, which are served through unicast transmissions.Nevertheless, differently from our work, this approach doesnot account for resource utilization and, like some otherinnovative works, may cause waste of resources. In a 5Gscenario, where several users require high quality services,a big issue is the limited availability of radio resources.Multicast transmissions have become a solution for bothincreasing network capacity and improving spectral efficiency.Hybrid unicast-multicast approaches [28] can provide anefficient radio resource exploitation.

Differently from previous works, this paper introduces autility-based network selection algorithm, which takes intoconsideration hybrid unicast-multicast transmissions and bal-ances energy consumption and quality for video deliveries inDenseNets. Besides taking into account the trade-off betweenthroughput and estimated energy consumption of the mobiledevice, the selection of the network is also affected by theradio resources required by the users, in order to achieve anefficient usage of radio spectrum. In particular, the approachproposed considers that users with good channel conditions,which consequently need less resources, could be served viaunicast, whereas users with bad channels can be served viamulticast. This paper extends an early version of the proposedHUMANS approach in [29] by: (i) introducing a compari-son in low and high density scenarios by adding users withhigher mobility (i.e., from 3 to 60 kmph), (ii) presenting theproposed idea through an algorithmic approach and presentingthe details of the HUMANS operation in a step-wise manner,and (iii) assessing the performance of HUMANS through anexhaustive simulation campaign under low and high densityconditions in terms of throughput, energy consumption, usersatisfaction, percentage of served users and utilized resources.

III. SYSTEM MODEL

The reference scenario consists of a DenseNet scenario, rep-resented by a LTE base station (eNB) and several small-rangeLTE femtocells (HeNB) under the same coverage area (Fig. 1).Multicast flows are activated within each cell belonging to thereference area. Users within this area access multimedia videocontent and pass through different cell coverages. In each over-lapping point users need to select the most appropriate networkto connect to.

In LTE systems [1], Orthogonal Frequency Multiple Access(OFDMA) and single carrier frequency division multipleaccess (SC-FDMA) are used to access the downlink and theuplink, respectively. The available radio spectrum is split intoseveral Resource Blocks (RBs) and, in the frequency domain,each RB corresponds to 12 consecutive and equally spacedsub-carriers. One RB is the smallest frequency resource that



Fig. 1. Example mobile users in a DenseNet environment with the presenceof multicast groups.

can be assigned to a user equipment (UE). The overall numberof available RBs depends on the system bandwidth and canvary from 6 (1.4 MHz channel bandwidth) to 100 (20 Mhz).The eNodeB (eNB), which is the node that communicates withUEs, is in charge to assign the adequate number of RBs to eachuser. The packet scheduler properly manages the transmissionparameters and the allocation of the B RBs according to theChannel Quality Indicator (CQI) feedbacks received from theusers. Based on the CQI received by each user, the trans-mission from the BS to the user is set with a given MCS. Foreach MCS level, a certain spectral efficiency is achieved by thetransmission. The greater is the spectral efficiency, the loweris the number of RBs required to achieve a given datarate.1

In case of multicast service, it is typically the UE thatexperiences the worst CQI that drives the MCS selection forthe multicast transmission. It means that the multicast flowis delivered with very low spectral efficiency. On the otherhand, during a multicast session all the bandwidth dedicatedfor the MBMS service could be assigned to the multicast trans-mission. Furthermore, it is worth noting that, according tothe eMBMS standard [6], at least 40% of whole availablebandwidth has to be dedicated to unicast transmissions.

We consider a wireless network scenario where differenttypes of small networks (the term cell is also used in thispaper), e.g., femtocells, are deployed in an uncoordinatedmanner within a macro cellular coverage, as shown in Fig. 1.

Let U = {ui|i = 1, . . . , n} the set of Users and C = {Cj|j =1, . . . , c} is the set of all cells of the scenario, and each cell Cj

can be either a eNodeB (i.e., a macrocell) or a HeNb (i.e., asmall cell). Since the handover decision measurements are per-formed in the downlink direction, we focus on the transmissionfrom a the generic cell Cj to a generic UE ui.

τ is the time interval (TTI) in between regular systemupdates. Every τ each i-th UE ui collects measurements fromall cells which it is able to sense.

1Depending on the spectral efficiency guaranteed by the MCS assignedto that transmission, the frequency scheduler has to decide how many RBsshould be assigned to the user.

Fig. 2. HUMANS procedures.

Network selection is then accomplished through comput-ing of a utility function U (eq. 1) that takes into account theenergy consumption of the mobile device when running real-time video applications, estimated network conditions, utilizedresources and estimated user’s satisfaction level.

IV. PROPOSED HUMANS ALGORITHM

The proposed approach aims to provide a very good trade-off between throughput, energy consumption and user satis-faction while allowing a high number of users to be served,hence targeting efficient resource utilization, too.

The proposed Hybrid Unicast-Multicast utility-basedNetwork Selection algorithm (HUMANS) is designed forDenseNet scenarios and is based on appropriate networkselection carried out by users. Moreover, according to the con-sidered scenario, the proposed algorithm could be tailored fornext-generation video applications. For example, the proposedHUMANS algorithm could be exploited for convergence ofbroadcasting and forthcoming 5G enabling technologies. Fig. 2presents a step-wise description of the algorithm phases.

• During step 1, each user first senses the neighbor cellsand send the CQI of the respective downlink channel toall of them.

• In step 2, each cell selects the most appropriate MCS levelfor the user according to the received CQI, and announcesthe multicast service (eMBMS Service announcement).In such a message is also included the MCS level of themulticast group. According to such informations the userperforms the network selection as explained in the nextstep.

• In step 3, Network selection is executed according to theutility function defined for each Radio Access Network(RAN) j by the following equation:

Uj = uωqqj ∗ uωe

ej∗ uωs

sj∗ uωb

bj(1)

The use of a utility function together with the MultiplicativeExponential Weighted (MEW) method in the decision makingmechanisms has proven to be useful in [30]. In equation (1)Uj is the overall score function for RAN j and uqj , uej , usj , ubj



are the utility functions defined for video service qual-ity, device energy consumption, user satisfaction and radioresource usage, respectively. wq, we, ws, wb are weightsfor the considered criteria, representing the importance ofthe associated parameter in the decision algorithm, wherewq + we + ws + wb = 1. Such score is computed for everyneighbor cells of each UE. This is done for both unicast andmulticast services, which could be offered within the cell. Thecell with the highest score is selected as target cell for the UE.Regarding multicast, it is considered that a multicast group isalready created in the cell and, if the considered UE will jointhe group, its transmission will adapt to the multicast trans-mission (i.e., it will be served with the MCS level alreadyselected for the multicast transmission). The novelty of theproposed approach is that it takes into account the multicasttransmission as an additional option during the RAN selec-tion. It means that, when sensing a new cell, each user exploitsthe opportunity to select either a unicast or a multicast trans-mission. Hence, a score is computed also for the multicasttransmission within the cell. In such a case if a user decidesto join a multicast group following the evaluation of eq. (1),then it could suffer from a lower performance in terms ofthroughput. This is due to the level determined by the leastchannel gain user in the multicast group, because it is assumedthat the scheduler implements the CMS scheme. On the otherhand, higher radio resource savings will be achieved since theresources for the multicast group have been already reserved.Therefore, the user joining a multicast group does not intro-duce additional resource waste. In such a way, the system hasmore resources available, i.e., more users could be served.

The utility function for the estimated video quality receivedby each RAN is defined by the following sigmoid utilityfunction introduced in [31]:

uq =

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

0, for Th < Thmin

1 − e

−α ∗ Th2

β + Th , for Thmin ≤ Th < Thmax

1, otherwise

(2)

The minimum throughput (Thmin) is a threshold to main-tain the multimedia service at a minimum acceptable qualitylevel. Values below this threshold result in unacceptable qual-ity levels. Threq is the required throughput in order to ensurehigh quality levels for the multimedia service. Whereas val-ues above the maximum throughput (Thmax) threshold will notadd any noticeable improvements in the user perceived quality.The quality utility has values in the [0,1] interval and no unit.In order to determine the exact shape of the utility functionthe values of α and β need to be calculated. Knowing that:(1) for Thmax = 3500 kbps the utility has its maximum value;(2) Threq = 250 kpbs; α and β are determined by performingsome mathematical computations of [31] and their values are1.64 and 0.86, respectively.

The estimated energy consumption for a real-time applica-tion is computed using equation (3), as defined in [32]:

E = t(rt + Threc ∗ rd) (3)

where t represents the transaction time, which can be esti-mated from the duration of the video stream; rt is the mobile

device energy consumption per unit of time (W), Threc isthe received throughput (kbps), rd is energy consumptionrate for data/received stream (J/Kbyte), and E is the totalenergy consumed (J). The two parameters, rt and rd, aredevice specific and differ for each network interface (e.g., LTE,WiFi) [33]. They were determined by running different sim-ulations for various amounts of multimedia data (i.e., qualitylevels) while measuring the corresponding energy levels andthen used to define the energy consumption pattern for eachinterface/scenario. The device power consumption depends onreceive (Rx) and transmit (Tx) power levels, uplink (UL) anddownlink (DL) data rate, and RRC mode [34]. Uplink transmitpower and downlink data rate greatly affect the power con-sumption, while uplink data rate and downlink receive powerhave little affect. In this work, we deal with the downlinkside, so we focus only on the power consumption contribu-tion related to the down link datarate. Therefore, the energyconsumption defined by eq. (3) refers only to the down-link datarate energy consumption. Based on the estimatedenergy consumption E, the utility for the energy criteria ue

is computed by using eq. (4) [15]:

ue =

⎧⎪⎨

⎪⎩

1, for E < EminEmax − E

Emax − Emin, for Emin ≤ E < Emax

0, otherwise

(4)

where Emin and Emax are computed considering Thmin andThmax, respectively.

The user satisfaction utility function us is defined as theratio between the datarate received and the datarate requiredby the user.

us = Threc

Threq(5)

Obviously, the satisfaction achieved by users connected viaunicast is, on average, closer to the value of 1 since the eNBtries to assign users all RBs they need. Oppositely, the satisfac-tion of users connected via multicast is affected by users withworse channel gain. Finally, the bandwidth utilization utilityreflects the amount of resources used by the user in the contextof the total amount of available resources. The utility is calcu-lated as the ratio between the new RBs used by the user andthe number of available RBs in the cell for the correspondingtype of transmission (i.e., unicast or multicast).

ub = 1 − RBused

RBavail(6)

In case of a multicast transmission such a percentage isequal to zero. Indeed, if a user joins a multicast group, nomore resources are used by the cell. In such a way, radioresources could be saved and, therefore, also the capacity ofthe system could be increased. The greater is ub, the higher isthe efficiency of the bandwidth utilization.

Eq. (5) and eq. (6) together represent the two factorsthat differentiate the selection between unicast and multicasttransmission within a cell.



TABLE IMAIN SIMULATION PARAMETERS

V. PERFORMANCE EVALUATION

An extensive numerical evaluation is conducted by usingMATLAB. The performance analysis is performed followingthe guidelines for the LTE system model in [35]. The mainsimulation parameters are listed in Table I. The parametersfor the LTE system are set according to [1]. The transmissionpowers of the cells have been such chosen in order to guar-antee a coverage area of about 500x500 m and 100x100 mfor macrocell and femtocells, respectively. The bandwidth of10 Mhz has been chosen in order to provide enough resources(i.e., 50 RBs) for efficient support of high-quality videomultimedia services (well known hungry-bandwidth applica-tions). The number of femtocells has been varied from 10 to 60in order to simulate variable environment conditions, from alow-density scenario to a high-density one, whereas the vari-ation of number of users (i.e., from 20 to 1000) allows usto validate the proposed approach under different traffic loadconditions.

Simulations have considered a reference scenario where sev-eral LTE femtocells are deployed within the coverage of a LTEmacrocell. It is assumed that a multicast group delivering theservice required by the user is already formed in each celland that such transmission is carried out with the minimumMCS level. This assumption guarantees that the users couldalways support such multicast transmission. A dense urbanscenario was considered where users are free to move accord-ing to Random Waypoint Mobility model [36]. Users’ speedvalues are uniformly distributed within the interval from 3km/h to 60 km/h. The simulations are carried out in a timeinterval of 3 minutes, with users downloading a real-timevideo. HUMANS algorithm performance is compared withthat of E-PoFANS [10] and EMANS [23]. Furthermore, in thepresented simulation campaign, the weights of all four utilityfunctions are considered the same (i.e., equal to 0,25). Thealgorithms’ performance has been computed every TTI, i.e.,the throughput Threc at the users and the relative energy con-sumption have been recorded at every TTI in the simulation.

To compare the three algorithms and to simulate the densescenario of the emerging 5G systems, simulation campaignshave been carried out in different network load conditions.

Simulation results, indeed, have been evaluated in High densityor Low density conditions (i.e., both users density and femto-cells density). The number of users has been varied from 20 to1000. The following simulation metrics have been considered:

• Average Throughput: the average quality of transmissionaccomplished to users;

• Aggregate Data rate (ADR): the sum of the throughputof the users among overall system;

• estimated Energy Consumption: the estimated energyconsumption of the devices when downloading a videoflow;

• User Satisfaction: the satisfaction perceived by users interms of the ratio between the datarate received and thedatarate required by each user;

• Percentage of served users: the measure of how efficientlythe algorithms work in terms of system capacity;

• Percentage of resource usage: the measure of the effi-ciency of the algorithms in order to save resources. Thelower is this metrics, the higher the performance;

• CQI variation: the distribution of users with differentCQIs among multicast and unicast transmissions. Thismetric is measured in terms of percentage of users servedwith a given CQI.

It is worth noting that the energy consumption of each userhas been calculated according to eq. (3) at every TTI. Threc isthe throughput received by users in the given TTI and t is theduration of the TTI.

A. Low Density

In this Section the performance of the three algorithms inlow density conditions are presented. Simulation results areshown in the case of 10 small cells within the macrocell. Theanalysis has been carried out with users moving at differentspeeds from pedestrian (i.e., 3 kmph) to low vehicular speed(i.e., from 30 to 60 kmph) in a dense urban scenario.

Fig. 3(a) shows the average throughput received by users.The proposed HUMANS algorithm outperforms the otherones, guaranteeing a relatively constant trend even whenincreasing the number of users within the reference area. Thisis due to the presence of the multicast groups, whose usersare always served with the same number of RBs and withthe minimum CQI experienced by group members. At thesame time both E-PoFANS and EMANS experience a decreasein their performance with an increasing number of users, asthese two algorithms use only unicast transmissions. This isexpected because the availability of resources decreases whenincreasing the network load. The system ADR achieved by thealgorithms is shown in fig. 3(b). Exploiting multicast commu-nications allows HUMANS algorithm to increase the ADR ofthe system when increasing the number of users. Indeed, eachnew user contribute to add rate to the system ADR. Whereasthe other two algorithms saturate after around 200 users withinthe system.

According to (1), also the estimated device energy con-sumption has to be taken into account (fig. 3(c)). For allalgorithms the highest energy consumption is met with fewusers in the system. That is because there are enough resources



Fig. 3. Low density scenario.

to serve users requiring higher datarate and greater resources,consequently consuming more energy. Compared to otheralgorithms, HUMANS achieves a gain ranging from 4% to 9%with respect to ePoFANS, whereas it gains up to 4% againstEMANS with a few users in the system.

Following the average throughput trend, the user satis-faction (fig. 3(d)) achieved by HUMANS is always high(i.e., around 80%), whereas the users satisfaction decreaseswith the number of users when adopting the two otheralgorithms. This is due to the limited amount of resources



Fig. 4. High density scenario.

for unicast transmissions considered in both EMANSand ePoFANS.

However, the strength of HUMANS is illustrated in bothfigures 3(e) and 3(f). The former shows how the proposedsolution is able to serve all users requiring access to the video

flow. This is due to the intrinsic behaviour of the multicastapproach, which can serve all users. Whereas, the two otheralgorithms have a limited capacity as they serve users via uni-cast only. At the same time, HUMANS also achieves resourceutilization savings between 25% and 15% compared to both



Fig. 5. Users’ CQI distribution.

EMANS and ePoFANS when increasing the density of thenetwork in terms of number of users.

B. High Density

The performance of the proposed algorithm has been evalu-ated also in a high density scenario, when the number of smallcells within the macrocell increases to 60.

Fig. 4(a) shows the average throughput received by users inthis high density scenario. Similar to the low density case,HUMANS outperforms the other solutions it is comparedagainst. Nevertheless, with few users ePoFans has still a verygood performance as, in these conditions, the high number ofcells deployed provides enough resources to satisfy all userrequests. However, E-PoFANS and EMANS decrease theirperformance with the increasing number of users. This hap-pens as these algorithms employ unicast transmissions onlyand an increase in the offered load adversely affects theirperformance.

Fig. 4(b) shows the ADR of the whole system for users inHigh density scenarios. At a certain point (500 users) EMANSand E-PoFANS do not bring any additional improvements,whereas HUMANS continues to follow the growing ADR.

This is due to the fact that multicast transmissions allowall users requiring the service to receive it with no addi-tional resource requirements. Indeed, as shown in Fig. 4(e)HUMANS provides overall coverage to all users in the system.On the contrary, EMANS and E-PoFANS suffer from high useroutage when increasing the overall number of users.

In the High density scenario, the performance of HUMANSin terms of energy consumption (Fig. 4(c)) shows a degra-dation with respect to the other algorithms. Since thereare many more resource available for users, both EMANSand ePoFANS are able to serve more users with lowerdatarate, and consequently low energy consumption, withrespect to the low density scenario. On the other hand, inHUMANS case, the unicast component is more prominent justbecause more resources are available, thus consuming moreenergy.

Whereas, as for users satisfaction (Fig. 4(e)), HUMANSmaintains the same trend of the low density scenario, asexpected, with higher achievable values (i.e., around and95%). Increasing the available resources in the systemallows the other two algorithms to achieve a performancecloser to HUMANS, but only with a few users in thesystem. When increasing the number of users, simulation



results show that HUMANS gains up to 65% (with 1000users).

All the above considerations are the result of a differ-ent behaviour of the algorithms in terms of resource usage(Fig. 4(f)). Since multicast transmission consumes many RBs,resource utilization is better for EMANS and E-PoFANS algo-rithms with a few users in the system. On the other hand,when increasing the number of users, these two algorithmsuse all the available resources, thus reaching saturation, whichleads to the high outage percentage illustrated in Fig. 4(e).Furthermore, as expected, in a high density scenario theincreasing number of cells leads to a consequent overallimprovement in the performance of all algorithms thanks tothe greater number of resources available.

C. Users’ CQI Distribution

The final discussion is about the distribution of user CQIlevels between multicast and unicast transmissions. Resultsare presented in Fig. 5, where the percentage of users servedfor each value of CQI is shown, for each kind of transmis-sion (multicast and unicast). Each bar related to the values onx-axis represents the percentage of users served with the CQIvalue associated. In all cases unicast transmission is activatedto users with high CQI levels (i.e., in good channel condi-tion) only. This is because users in good channel conditionrequire a few RBs, whereas users experiencing bad channelconditions need more resources to obtain the required datarate.In HUMANS, the users with lower CQI levels are servedvia multicast transmissions and this has a double advantage:(i) they do not waste additional resources and (ii) make useof multicast flows (i.e., they receive all the RBs dedicated tothe multicast group). Therefore, thanks to this approach, usersrequiring many resources that cannot be served if an only-unicast oriented algorithm is implemented, can always receivethe video service, especially when the system is in high loadconditions.

Fig. 5 best depict the objective of the proposed HUMANS,that is to guarantee an increasing user capacity either by usingthe multicast or by saving resources.

VI. CONCLUSION

This paper proposes the Hybrid Unicast-Multicast utility-based Network Selection algorithm (HUMANS), a networkselection approach that exploits the benefits of co-existingunicast and multicast transmissions during video deliveries.

HUMANS considers bandwidth utilization and the trade-off between quality and energy consumption when deliveringvideo in DenseNet scenarios.

A major contribution of HUMANS is the considerationof joining a multicast group as a possible option in thenetwork selection process, thus allowing for smart bandwidthmanagement. HUMANS serves users with good channel con-ditions via unicast transmissions and the remaining users viamulticast.

Performance evaluation carried out in low- and high-densityscenarios, demonstrate how the proposed hybrid unicast-multicast approach provides a significant improvement in

terms of capacity and radio resource utilization in compari-son with other unicast-only solutions. The performance gain ismuch higher when user density increases within a system, thusproviding an interesting solution for the future dense networks.

Future extensions of this work will account for the variationof both the background traffic in the network and the weightsassigned to each utility. Finally, the proposed algorithm willbe deployed in future dense wireless networks scenarios thatwill be one of the enabling technologies of the forthcoming5G system. It could also exploiting other solutions, such asDevice-to-Device (D2D) communications for network trafficoverloading and innovative management of the least channelgain users. Furthermore, an actual hot-topic is the conver-gence of broadcasting and diverse 5G enabling technologies.HUMANS approach, based on group-oriented communica-tions could be a suitable solution for the support of broadcastvideo transmission over cellular networks.

ACKNOWLEDGMENT

Special thanks go to Angelo Tropeano for his valu-able contribution to the simulation activities. The EuropeanUnion funding for the NEWTON project under theHorizon 2020 Research and Innovation Programme withGrant Agreement no. 688503 is gratefully acknowledged(http://newtonproject.eu).

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Giuseppe Araniti (S’03–M’05–SM’15) received theLaurea degree and the Ph.D. degree in electronicengineering from the University Mediterranea ofReggio Calabria, Italy, in 2000 and 2004, respec-tively, where he is an Assistant Professor of telecom-munications. His major area of research includespersonal communications systems, enhanced wire-less and satellite systems, traffic and radio resourcemanagement, multicast and broadcast services,device-to-device and machine-type communicationsover 5G cellular networks.

Pasquale Scopelliti (S’15) received the M.Sc.degree in telecommunications engineering from theUniversity Mediterranea of Reggio Calabria, Italy,in 2013, where he is currently pursuing the Ph.D.degree. His current research interests include cellu-lar systems, radio resource management, multicastand broadcast services over 5G cellular networks,and heterogeneous networks.

Gabriel-Miro Muntean (S’02–M’04–SM’17)received the Ph.D. degree from Dublin CityUniversity (DCU), Ireland, in 2003, where heis an Associate Professor with the School ofElectronic Engineering and the Co-Director of theDCU Performance Engineering Laboratory. He haspublished over 300 papers in prestigious interna-tional journals and conferences, has authored threebooks and has edited six other books. His currentresearch interests include quality-oriented andperformance-related issues of adaptive multimedia

delivery, performance of wired and wireless communications, and personal-ized technology-enhanced learning. He is an Associate Editor of the IEEETRANSACTIONS ON BROADCASTING, the Multimedia CommunicationsArea Editor for the IEEE COMMUNICATIONS SURVEYS AND TUTORIALS,and Reviewer for other top international journals, conferences, and fundingagencies. He coordinates the EU Horizon 2020 project NEWTON and is asenior member of the IEEE Broadcast Technology Society.

Antonio Iera (SM’07) received the graduationdegree in computer engineering from the Universityof Calabria, Italy, in 1991, the Master Diplomadegree in information technology from CEFRIEL,Politecnico di Milano, Italy, in 1992, and the Ph.D.degree from the University of Calabria, in 1996.Since 1997, he has been with the University ofReggio Calabria and currently holds the positionof Full Professor of telecommunications and theDirector of the Laboratory for Advanced Researchinto Telecommunication Systems. His research

interests include next generation mobile and wireless systems, RFID systems,and IoT.

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