A Novel Approach for Unicast and Multicast Traffic ...

A Novel Approach for Unicast and Multicast Traffic Management in Wireless Networks S. Pizzi, M. Condoluci, G. Araniti, A. Molinaro, A. Iera DIIES, University “Mediterranea” of Reggio Calabria Via Graziella, Loc. Feo di Vito, 89100, Reggio Calabria, Italy Email: [sara.pizzi | massimo.condoluci | araniti | antonella.molinaro | antonio.iera]@unirc.it

Abstract—The rapid demand growth for group-oriented services mandates telco operators to efficiently manage the contemporary presence of the unicast services and the emerging multicast applications. We propose a novel radio resource management approach for providing fair throughput to unicast and multicast users in a broadband wireless network. The idea is to assign unicast subscribers to a virtual group in order to compete on an equal footing with multicast users for network resource distribution. We also revisit the traditional fairness indexes in order to capture the fairness in the degree of user satisfaction for unicast and multicast users. Simulations highlight the effectiveness of the virtual group strategy in terms of achieved fairness in several scenarios and under different traffic loads. Index Terms—Unicast, Multicast, Resource Allocation, Broadband Wireless Access, LTE, WiMAX

I. I NTRODUCTION The demand for wireless multimedia services such as mobile IPTV, IP radio broadcasting, has been increasing in the last few years. On the one hand, this may be considered as a big source of revenue for wireless Internet service providers; on the other hand, it is also a source of challenges for broadband wireless access (BWA) operators that are called to manage massive requests of multicast services beside traditional unicast traffic like voice, web browsing, and so on. WiMAX (Worldwide Interoperability for Microware Access) [1] and LTE (Long Term Evolution) [2] networks represent the most accredited among available BWA technologies. Providing satisfactory services to unicast and multicast subscribers is challenging because of the space- and timevarying nature of the radio channel in wireless systems, the diversity of service requirements and the limited spectrum. BWA systems typically use adaptive modulation and coding (AMC) techniques by dynamically choosing the modulation and coding scheme (MCS) that is more suitable to the measured channel quality. Moreover, radio resource management (RRM) techniques implemented by network providers for efficient traffic delivery strongly depend on how (i) channel quality information (CQI) is utilized, and (ii) network resources are assigned to unicast and multicast traffic. Differently from the unicast traffic delivery, where the choice of the MCS depends on the measured CQI on individual base station-to-user equipment (BS-to-UE) links, in the multicast case the transmission parameters are selected by the BS on a per-group basis. Multicast group members may measure different radio link qualities and, consequently,

support different MCSs. The multicast member with the worst channel condition imposes the MCS selection for the entire group, thus becoming the bottleneck of the group. Distributing radio resources fairly between unicast and multicast traffic is a challenging issue because these are characterized by different constrains and requirements that in some cases are in opposition to each other. For instance, favoring multicast traffic permits a provider to achieve higher network utilization and spectrum usage (i.e., low amount of resources needed to serve a large set of destinations), because the point-to-multipoint (PMP) transmission mode allows to simultaneously feed the whole multicast group composed of more users. Nevertheless, it may have negative consequence on the unicast users’ satisfaction degree since unicast users could be deprived of resources, especially if many multicast groups are active in a cell. On the contrary, favoring unicast traffics may quickly waste the available resources. This may involve a service degradation for multicast flows, which cannot achieve resources. Furthermore, traditional performance indexes designed to measure the throughput fairness among the users served in the cell should be adjusted in scenarios with mixed unicast and multicast traffics to capture the degree of user satisfaction. Albeit many works in literature deal with multicast transmission in BWA networks, insufficient attention has been put so far on the effective handling of mixed unicast and multicast traffic in a cell coverage area. In general, allocating resources to unicast and multicast traffic is an NPhard problem that requires a prohibitive computation burden in the BS to solve it, as demonstrated in [3]. In this paper, we propose a radio resource management scheme, called Virtual Unicast Group (VUG), based on a heuristic algorithm implemented at the BS for efficiently managing unicast and multicast subscribers. In particular, VUG aims to achieve fair throughput allocation to unicast and multicast users. The main idea of VUG is to clusterize unicast destinations in virtual groups in order to compete for resource distribution on an equal footing with multicast traffic. The main contributions of this paper are: (i) the idea of using virtual groups for the management of unicast users under the BS control, (ii) the design of a resource assignment policy to fairly handle unicast and multicast traffic in a cell; (iii) a wide simulation campaign aimed at finding the number and the size of virtual groups in a cell that achieve the best fairness in terms of user satisfaction.

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II. R ELATED W ORK There is a lack of effective algorithms that handle a mixed unicast and multicast traffic in the same BS coverage area. Indeed, different approaches can be used to manage resources allocation to unicast users (e.g., throughput maximization, proportional fairness, and so on), whereby the choice of the MCS is performed by the link adaptation function on a peruser basis. On the contrary, multiple destinations in the same multicast group can be fed through a single PMP transmission by exploiting the broadcast nature of the radio channel [4], and the MCS parameters are selected on a per-group basis. As a consequence, the presence of cell-edge users forces the BS to use low-data rate MCSs in order to reach all receivers in the cell with a robust transmission. The main benefit of this technique, named here Conventional Multicast Scheme (CMS) [5], is that resources are fairly distributed among the multicast group members that will achieve the same data rate. When we focus on the related literature on joint management of unicast and multicast traffics, we can identify three main trends that here we call: Unicast Maximization (UM), Equal Sharing (ES), and Equal Competition (EC). The first trend privileges unicast traffic; the second one aims to equally share the available resources between unicast and multicast users; and the third one aims to maximize the number of conveyed bits to improve the spectrum utilization. Example of proposals which belong to the UM philosophy in multi-carrier OFDM (Orthogonal Frequency Multiplexing) systems can be found in [6] and [7]. The main idea is to guarantee the minimal required data rate to all multicast users, based on the CMS logic, and then to assign the remaining resources to unicast users to maximize the throughput. As a consequence, benefits (i.e., high throughput) are guaranteed to unicast destinations at the expense of multicast receivers which experience significant quality limitations. An example of the ES strategy can be found in [8], which presents a power-saving scheduling algorithm that manages mixed unicast and multicast traffic. According to the ES philosophy, multicast and unicast services equally share the network capacity; the resources to be assigned to the two types of traffic (unicast and multicast) are statically split into two sets. This policy prevents one traffic class to utilize the resources assigned to the other traffic class but its main inefficiency is due to the static bandwidth assignment that cannot adapt to the variation of load of unicast and multicast users in the cell, thus resulting in inefficient spectrum utilization and possible outage of one traffic class. Finally, a technique that follows the EC logic and aims to guarantee the minimum data rate of both unicast and multicast flows is proposed in [3]. The extra resources are assigned according to a maximum throughput scheme, which iteratively selects the (unicast or multicast) service that conveys the highest number of bits to the destinations. With the aim to maximize the system throughput, this technique intrinsically gives higher priority to multicast services, since multiple destinations can be satisfied with a single transmission. There-

fore, multicast users have higher probability to be served with respect to unicast users, which may suffer from poor throughput performance. The above considered issues ask for an effective strategy for joint multicast and unicast traffic management, which is the aim of the proposed radio resource management techniques presented in Section III-A. In addition, in Section III-B we define a new fairness index that aims to capture the extent to which unicast and multicast users are equally satisfied. III. T HE VUG

PROPOSAL

We refer to a single-BS coverage area where both unicast and multicast users are interested in receiving real-time video streaming, where a video stream is split into a base layer and more enhancement layers. A. The VUG strategy In the depicted scenario, we propose the Virtual Unicast Group (VUG) algorithm that aims to efficiently and fairly assign the downlink radio resources in a cell to unicast and multicast users. The minimum goal is to guarantee the delivery of the base layer to all the users (unicast and multicast) in the cell by using an iterative algorithm implemented at the BS. The main idea behind VUG is to organize unicast users in one or more virtual groups, managed by the BS in analogy to multicast groups to allow unicast users to compete for network resources allocation with multicast groups on an equal footing. A group is named “virtual” since unicast members are not interested in the same type of data. The number of activated virtual groups in a cell is a system parameter tunable by the network operator based on traffic conditions, as explained in the following of this Section. The operation of VUG can be summarized through the sequence of the following steps. Step 1 - Channel state collection. At the beginning of each scheduling frame, the BS collects information about the channel conditions (CQIs) perceived by each user equipment u located in the cell and computes the related sustained MCS mu , ∀u ∈ U. This step is repeated every scheduling frame with the aim to adapt the transmission parameters according to the radio channel variations experienced by the served users. Step 2 - Groups formation. In this step, the BS splits the user set U into different groups. In particular, all the users interested in a given multicast service will join the same group g ∈ MG, and form the users set UMg . Analogously, unicast users are grouped into virtual groups. In a virtual group g ∈ VG, each user u ∈ UV g may ask for a different video service. The number and the size of virtual groups to activate is decided by the BS considering fairness in the treatment of unicast and multicast users as the main design parameter. In general, a variable number of VGs can be generated according to the traffic loads and the degree of fairness that the network operator aims to provide. For the sake of simplicity, we consider that only one multicast group is active in the cell. The underlying assumption of VUG is that if the number of unicast users per cell is lower than the number of multicast

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users, then only one virtual group is created. If the unicast users are more numerous than the multicast users, VUG can follow three simple schemes for virtual groups creation: •

•

•

VUG static (VUG-S): only one virtual group of size N is activated per cell irrespective of the number of multicast users. VUG homogeneous (VUG-H): multiple virtual groups of the same size are created in the cell. A new virtual group is activated each time the number of unicast users exceeds the number of users in the multicast group. VUG maximum (VUG-M): multiple virtual groups are created in the cell. The size of the groups is chosen equal to the number of multicast users as far as possible; specifically if the number of unicast users is an integer multiple of the multicast group size, then all the virtual groups groups will have a size equal to the number of multicast users, otherwise only one virtual group will have a size lower than the multicast group size.

As an example, if a multicast group composed of 10 users and 16 unicast users are active, 2 virtual groups are activated under both VUG-H and VUG-M, while VUG-S always enables a single virtual group. The difference between VUG-H and VUG-M is that the former enables two virtual groups composed by 8 users, whilst the latter creates one group of 10 users and one group of 8 users. The simulation campaign in Section IV shows that the best choice is given by the VUG-M policy, i.e., the virtual group(s) size has not to be greater than the size of the multicast group in the cell. We demonstrated that higher (lower) values of the virtual group(s) size correspond to greater (smaller) advantages to the unicast traffic. Step 3 - Base layer allocation. The BS allocates to all (multicast and unicast) users the amount of resources required for the delivery of the base layer of the related video service. We assume that the minimum service must be assured to all users in the cell; this means that each group (virtual and multicast) is assigned at least the base layer of the requested video service. VUG determines the amount of resources denoted by rg,m required to transmit the base layer of the requested video service s to each generic group g (multicast or virtual), as in Eq. (1):

   mu ⌉, ⌈ds,u,1 /c min u∈U Mg X X rg =  ⌈ru ⌉ = ⌈ds,u,1 /c(mu )⌉,   u∈U V g

if g ∈ MG if g ∈ VG

u∈U V g

(1) with mu ∈ {1, 2, . . . , C} being the maximum MCS sustained by user u in the group, ds,u,1 the number of bits required for the transmission of the base layer (layer 1) of the video service s requested by user u, and c(·) the transport block size (i.e., number of bits per resource unit) for a given MCS. The amount of resources (resource units) for the multicast group is calculated on the basis of the number of bits for the base layer of the requested video service ds,u,1 (the same

for all users) and the transport block size related to the MCS of the user in the worst channel conditions. Differently, the resources for a virtual group are the sum of the resources assigned to each single unicast user in the group, referred as ru . The resources given to a single user depend on the amount of bits for the base layer of the video service requested by the user and the transport block size related to the MCS of that user. Step 4 - Enhancement layers allocation. The BS iteratively selects the group (virtual or multicast) for providing the enhanced layers. The procedure stops when all available radio resources are allocated on a scheduling frame basis. If there is still available bandwidth after the allocation of the base layer to all the users in the cell, then the BS iteratively determines the multicast or virtual group to serve and the amount of resources to assign to the selected group. The policy implemented for the eligible group selection is up to the network provider, since VUG is independent from the strategy utilized. However, in this paper, we assume that the BS selects the group that maximizes the aggregated data rate for the system. The decision is taken, layer by layer, iteratively until resources are available in the current scheduling frame. For each set of users in a multicast or virtual group, the BS first computes the requested amount of bits sdg as in Eq. (2) and then selects the group to serve and assign the resource to it:  Ls X    mu , ds,u,l − rg c min   u∈U Mg l=1 sdg = Ls X X   1  ds,u,l − ru c(mu ) ,   |U V g | u∈U V g


l=1

(2)

For a multicast group, the requested number of bits is computed as the difference between the total number of bits needed PLs for the delivery of all the video stream’s layers (i.e., l=1 ds,u,l )and the amount of bits already allocated (i.e., mu )). rg c( min u∈U Mg

In the case of a virtual group, the requested number of bits is computed as the average amount of bits necessary to deliver the remaining requested video streams to the virtual group members. For each unicast user, the requested data is dependent on the amount of resources already allocated ru c(mu ). The operation of averaging the data rate of unicast users |U 1V g | is introduced in order to assimilate the operation of the virtual group to a multicast group. In such a way, the requested data rate can be considered as only one for the entire group. Once the per-group required resources are determined, the algorithm selects the group g ∗ operating at MCS m∗ that maximizes the system throughput as follows:

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S

g∗

  arg max sdg · |UMg | ), g∈MG =  arg max sdg · |UV g | ), 


(3)

g∈VG

The resources allocated to the selected group are calculated as in Eq. (4): (4) min rg∗ = ⌈sdg∗ /c mu ⌉ u∈U Mg∗

If a multicast group is selected, the algorithm simply assigns the amount of resources to deliver the requested data rate to the selected group, and deletes the served group from the multicast group set in case it is served with the maximum sustained data rate. In the case that a virtual group is selected, the scheduled resources have to be successively distributed among the unicast users in the group. Different strategies can be implemented, this representing one more flexibility factor of VUG. In this paper, we have chosen a maximum throughput approach, so that the BS iteratively selects the user with the best channel condition, i.e.:  X Ls    u∗ = arg min ds,u,l − ru c(mu ) /c(mu ) u∈Sg∗ l=1    s.t. u = arg max mu

Channel bandwidth Frequency band DL/UL ratio Cyclic prefix time Nr. of subcarriers Frame duration Supported MCS

7 MHz 3.5 GHz 150/303 1 µs 256 10 ms BPSK 1/2, QPSK (1/2-3/4), 16QAM (1/2-3/4), 64QAM (2/3-3/4) TDD 5s (500 frames)

(5)

This assignment is iterated until all the resources for the selected virtual group are available. B. The proposed fairness index We define a novel Fairness Index (FI) that is tailored to measure the fairness in the degree of user satisfaction, i.e., the ratio between the assigned data rate and the maximum sustained data rate per type of user (unicast and multicast). The proposed FI defined as follows: tuni avg mul tuni avg + tavg

TABLE I OFDM PHY S PECIFICATIONS

Duplexing mode Simulation duration

u∈Sg∗

FI = 1 −

The Medium Access Control (MAC) layer uniquely identifies each traffic flow by a Connection Identifier (CID). In addition, the BS may establish a multicast service by creating with each SS a multicast connection to be associated with the service. The physical layer is based on Orthogonal Frequency Division Multiplexing (OFDM) and Time Division Duplex (TDD). The downlink subframe is assigned a maximum number of 150 OFDM symbols out of the 303 symbols available for each TDD frame [1] to account for the presence of uplink traffic. Under this assumption the downlink capacity goes from about 3Mbps (for BPSK with 12 coding scheme) to more than 26Mbps (for 64QAM 34 ). OFDM PHY layer specifications are reported in Table I.

(6)

mul where tuni avg and tavg represent, respectively, the mean satisfaction degree achieved by unicast and multicast traffic. Our FI takes values in the range [0, 1]. In the case of perfect fairness, i.e., unicast and multicast users achieve the same satisfaction, FI is equal to 0.5. It is higher than 0.5 in the case multicast traffic obtains higher satisfaction. On the contrary, it is lower than 0.5 if the satisfaction of unicast users is higher.

IV. S IMULATION R ESULTS The performance of VUG have been examined through Matlab over a WiMAX network centrally managed by a BS that offers connection-oriented services to fixed subscriber stations (SSs). In particular, the reference topology includes a BS located in the center of a 1000m x 1000m grid and a variable number of SSs uniformly distributed across the grid.

Channel features are simulated by the Stanford University Interim (SUI-6) channel model [9] in hilly terrain with moderate-to-heavy tree densities (Type A). The model accounts for both macroscopic channel effects, such as path loss and shadowing, and microscopic effects, such as multipath fading, which is modeled as a tapped delay line with 3 taps with non-uniform delays and maximum Doppler frequency. In order to cope with the channel variability, on a scheduling frame basis the BS dynamically chooses the MCS by comparing the reported CQI feedback on each BS-to-SS link against the allowed values of Signal-to-Noise Ratio (SNR) and the minimum input level sensitivity at a given bit error rate (BER) [1]. The VUG strategies presented in this paper have been evaluated through Matlab simulations and obtained results have been compared with the three baseline approaches described in section II, Unicast Maximization (UM), Equal Sharing (ES) and Equal Competition (EC) Each simulation run has been repeated several times to get 95% confidence intervals in the presented results. The performance of considered policies are evaluated in terms of the proposed FI. Furthermore, the Average User Throughput (the achieved bit rate per user accounting for the packets successfully delivered to the target SSs during the simulation time) and the Aggregate Data Rate (ADR) (the system throughput measured as the sum of the throughput experienced by all the users) are considered in the simulation results. We analyze the performance of VUG strategies when one multicast group of fixed dimension and a variable number of

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unicast flows are active in the cell. The size of the multicast group is set equal to 20, while the number of unicast users goes from 10 to 50. All users are uniformly distributed within the cell, and request video flows with the same characteristics, i.e., with one base layer and four enhancements layers (with a data rate varying from 120 to 564 kbps). Figure 1 shows the performance of the algorithms in terms of the proposed Fairness Index. The effectiveness of the metric is testified by the behavior of the traditional algorithms used as benchmarks. Indeed, ES and EC achieves a FI higher than 0.5., i.e., multicast users are more satisfied than the unicast ones. On the contrary, the general trend of UM is to favor unicast users (FI is lower than 0.5), but when the unicast load is high UM reaches a performance close to 0.5 (i.e., equal satisfaction). This is due to the fact that almost all the resources are used to guarantee the minimum data rate to unicast and multicast flows and, consequently, no further resources for enhancements layers are available. We can clearly appreciate how the proposed VUG-M is the most effective algorithm in carrying into effect a fair throughput distribution between unicast and multicast traffic. Indeed, it achieves a FI performance close to 0.5 in all the cases, and outperforms VUG-S and VUG-H. In detail, VUG-S has the same behavior of VUG-M starting from 30 unicast users, while VUG-H does not follow a stable trend and its FI fluctuates between 0.45 and 0.7. 1

EC UM ES VUG−S VUG−H VUG−M

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16

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14

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Fairness Index

0.7

extent, to limit the disadvantages of unicast flows at the expense of multicast traffic. All the proposed VUG techniques are able to further improve the performance of unicast traffic compared to EC and ES; on average, our proposals achieve a gain of 14% compared to the EC, whereas the divergence between our solutions and UM is about 9%. It is worth noticing that when the number of unicast users in the cell is larger than 30, the proposed VUG-M outperforms the UM in terms in unicast throughput. Focusing on the multicast throughput, our solutions offer a trade-off between the ES and the UM. In particular, we observe that VUG-S and VUG-M obtain the same behavior of UM starting from 30 and 40 unicast users, respectively. A different performance is achieved by VUG-H, which is strongly influenced by the number of unicast users. When virtual groups are smaller than the multicast group, the multicast throughput is higher compared to other VUG policies. In Figure 3 the ADR performance is reported under uniform users distributions. It is important to highlight that ES follows a linearly increasing trend, since with this approach the multicast group is always able to get the resources needed to deliver all enhancements layers. On the contrary, UM shows the worst performance since favoring unicast users is not useful to increase the overall ADR. Our VUG schemes represent an intermediate solution between EC and UM. It is worth to highlight that VUG-M and VUG-H outperform the UM strategy, while VUG-S has the same performance of UM starting from 30 unicast users.

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FI under increasing number of unicast users.

In Figures 2 the mean throughput of unicast (Figure 2(a)) and multicast (Figure 2(b)) users is shown when increasing the number of unicast users. The achieved results confirm the behavior attained through the proposed FI. We can observe that UM offers the highest throughput to unicast users and the worst performance to multicast members (only minimum performance is assured, i.e., the base layer). Conversely, ES achieves the poorest results for unicast flows and the highest ones for multicast users. Indeed, unicast and multicast traffic have the same amount of resources assigned, but, while this is sufficient for multicast flows that can benefit of a single transmission towards multiple users, it is scarce for satisfying the requests of an increasing number of unicast users. The EC approach is able, at some

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Fig. 3.

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ADR under increasing number of unicast users.

V. C ONCLUSIONS In this paper we have analyzed the problem of fairly distributing radio resources between unicast and multicast traffics in broadband wireless access networks. We have proposed VUG (Virtual Unicast Group), an algorithm that organizes unicast users into virtual groups that are managed by the BS in analogy to multicast groups, thus allowing unicast users to compete on an equal footing with multicast users for network resource distribution. We have designed a novel Fairness Index metric to measure the fairness in the satisfaction of unicast and multicast users and we have presented different strategies

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Throughput of (a) unicast, and (b) multicast users under increasing number of unicast users.

for virtual groups creation. The results showed that forming virtual groups with the same size of the multicast group is the most effective approach to achieve fair treatment of the users in a cell. R EFERENCES [1] IEEE 802.16 - Standard for Air Interface for Broadband Wireless Access Systems, 2012. [2] 3GPP, TS 36.300, “Evolved Universal Terrestrial Radio Access (EUTRA) and Evolved Universal Terrestrial Radio Access Network (EUTRAN)”, Rel. 11, September 2012. [3] Hui Deng, Xiaoming Tao, and Jianhua Lu, “Qos-aware resource allocation for mixed multicast and unicast traffic in OFDMA networks”, EURASIP Journal on Wireless Communications and Networking, 2012. [4] A. Richard, A. Dadlani, and K. Kim, “Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey”, IEEE Communications Surveys and Tutorials, 15:1, pp. 240-254, 2013. [5] J. Liu, W. Chen, Z. Cao, and K. Letaief, “Dynamic power and subcarrier allocation for OFDMA-based wireless multicast systems”, IEEE International Conference on Communications (ICC08), May 2008. [6] Hanbyul Seo, Seoshin Kwack, and Byeong Gi Lee, “Channel Structuring and Subchannel Allocation for Efficient Multicast and Unicast Services Integration in Wireless OFDM Systems”, Globecom 2007. [7] Jun Shen, Na Yi, Bo Wu, Wei Jiang, and Haige Xiang, “A greedybased resource allocation algorithm for multicast and unicast services in OFDM system”, WCSP 2009. [8] Chaoan Wu, Xuekang Sun, and Tiankui Zhang, “A power-saving scheduling algorithm for mixed multicast and unicast traffic in MBSFN”, ComComAp 2012. [9] “Channel models for fixed wireless applications”, IEEE 802.16 Broadband Wireless Access Working Group, June 2003.