R-letter of August 2013 - IEEE Communications Society

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IEEE COMSOC MMTC R-LetterFairness Resource Allocation in Blind Wireless Multimedia CommunicationsA short review for “Fairness Resource Allocation in Blind Wireless Multimedia Communications"Edited by Koichi AdachiL. Zhou, M. Chen, Y. Qian, and H.-H. Chen, "Fairness Resource Allocation in BlindWireless Multimedia Communications," IEEE Trans. on Multimedia, vol. 15, no. 4, pp.946-956, Jun. 2013.A scenario where one base station (BS) assignsavailable resource to multiple multimedia usersis considered. The fairness resource allocationproblem is formulated with a fairness parameter to maximize summation of user’s quality ofexperience (QoE). Traditional -fairnessresource allocation in wireless multimediacommunication systems assumes the QoE model(or utility function) of each user is available atscheduler. In such a system, balancing thetradeoff between the fairness and performance isan important task for BS as it is known that theintroduction of fairness generally has a negativeimpact on the performance [1-4]. However, thecritical assumption in most existing studies is theavailability of QoE model at the scheduler,which is may not be practical.Therefore different from previous works, a blindscenario is considered in this paper where BS hasno knowledge of the QoE model during thewhole resource allocation procedure. Theanswers to the following two questions areprovided in this paper: 1) How to set the fairnessparameter from the perspective ofperformance-fairness tradeoff? 2) Given aspecific fairness parameter , how to implementthe -fairness resource allocation online?The main contributions of this paper are lying in:Qualitative analysis and Technical realization.Some recent works theoretically analyzed theperformance with special cases of a value, e.g.,proportional fairness ( → 1) and max-minfairness (→∞) [1-3,5,6]. For more general caseof , only empirical modelling was provided [2].An exact expression for the upper bound of theperformance loss is caused by -fairness,characterizing the fairness-performance tradeoff(Theorem 1). This enables a BS scheduler tochoose the appropriate fairness parameter which answers the first question.For technical realization of a specific fairnessresource allocation, convex optimization [4], [7-10] and game theory [10-15] are generally usedin previous works. However, both approachesrequires the utility function of each user to beavailable at BS or the controller. In this paper, ablind fairness-aware resource allocation problemis decomposed into two subproblems to describethe behaviors of the users and BS. The secondquestion is answered by proposing a biddinggame for the reconciliation between the twosubproblems. The authors show that although allthe users behave selfishly, any specific a-fairnessscheme can be implemented by the bidding gamebetween the users and BS (Theorem 2).In Theorem 1, the upper bound of theperformance loss incurred by the -fairness isderived. The derived upper bound connects thenumber of users in the system and the fairnessparameter and it is independent from the QoEmodel as long as it satisfies some assumption,which is not too restrictive. The authors proposea bidding game to decompose the optimizationproblem into two subproblems similar to [16].The first subproblem describes the behaviors ofthe users and the second the BS, respectively.Each user tries to maximize its own objectivefunction by bidding game. Then, for givenpayment from each user, the BS strives to findoptimal transmission rate for each user tomaximize its own objective function (controlfunction). The control function is composed ofthe bidding money, allocated resource, andfairness parameter . In Theorem 2, the formatof control function is given to resolve theoriginal optimization problem. To make theproposed bidding game work smoothly in arealistic blind scenario, the assumption that eachuser does not cheat during the whole biddingprocess is required. The authors also provide thecounter-measures for this issue. The convergenceproperty of the proposed bidding game is givenin Theorem 3.The performance evaluation of the proposedbidding game is performed based on real-worldtraces consisting of three multimediaapplications: audio, file, and video. Forhttp://committees.comsoc.org/mmc 10/22 Vol.4, No.4, August 2013
IEEE COMSOC MMTC R-Lettercomparison, the resource allocation with fullinformation,where the BS knows the QoE modelof each user in advance, is considered [17].Firstly, the accuracy of the derived upper boundof the loss function is confirmed by comparing itwith the real observed loss values. It is shownthat the real loss value is close to the obtainedupper bound for different value of . It is alsoconfirmed that a higher value of yields ahigher performance loss, which is consistentwith the previous works [2]. Furthermore, largernumber of users also incurs a larger performanceloss. This observation suggests a basic operationrule for BS: when the system has a relativelysmall number of users, BS can achieve fairallocations without significant performancedeterioration. However, in the case with a largenumber of users, BS should be careful to employfairness as it will easily lead to a largeperformance loss. Secondly, the total MOS valueof the proposed bidding game scheme iscompared with those of the full-information case.The proposed bidding game almost is shown toachieve the same performance as the fullinformationcase when is large. Finally, theconvergence property of the proposed biddinggame is clarified that 1) it converges within alimited number of iteration and 2) the number ofusers affects the convergence time.The derived upper bound of the performance lossincurred by the -fairness is useful tocharacterize and understand the tradeoff betweenthe performance loss and the fairness. Since theproposed bidding game does not requireknowledge of QoE of each user, it is applicableto practical multimedia communication system.References:[1] T. Lan, et al., An Axiomatic Theory ofFairness in Resource Allocation, Princeton,NJ, USA: Princeton Univ., 2009.[2] L. Kaplow and S. Shavell, Fairness VersusWelfare, Cambridge, NJ, USA: HarvardUniv., 2002.[3] L. Zhou, et al., “Distributed media services inP-based vehicular networks,” IEEE Trans.Veh. Technol., vol. 60, no. 2, pp. 692–703,Feb. 2011.[4] L. Zhou, et al., “Distributed schedulingscheme for video streaming over multichannelmulti-radio multi-hop wirelessnetworks,” IEEE J. Sel. Areas Commun., vol.28, no. 3, pp. 409–419, Mar. 2010.[5] T. Nguyen, et al., “Efficient multimediadistribution in source constraint networks,”IEEE Trans. Multimedia, vol. 10, no. 3, pp.523–537, Apr. 2008.[6] Y. Li, et al., “Content-aware distortion-fairvideo streaming in congested networks,”IEEE Trans. Multimedia, vol. 11, no. 6, pp.1182–1193, Oct. 2009.[7] D. Hu, et al, “Scalable video multicast incognitive radio networks,” IEEE J. Sel. AreasCommun., vol. 28, no. 3, pp. 334–344, Mar.2010.[8] H.-P. Shiang and M. v. d. Schaar,“Information-constrained resource allocationin multi-camera wireless surveillancenetworks,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 20, no. 4, pp. 505–517, Apr.2010.[9] Y. Zhang, et al., “Multihop packet delaybound violation modeling for resourceallocation in video streaming over meshnetworks,” IEEE Trans. Multimedia, vol. 12,no. 8, pp. 886–900, Dec. 2010.[10] H. Hu, et al., “Peer-to-peer streaming oflayered video: Efficiency, fairness andincentive,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 21, no. 8, pp. 1013–1026, Aug.2011.[11] Z. Han, et al, “Fair multiuser channelallocation for OFDMA networks using Nashbargaining solutions and coalitions,” IEEETrans. Communun., vol. 53, no. 8, pp. 1366–1376, Aug. 2005.[12] H. Park and M. van der Schaar, “Fairnessstrategies for wireless resource allocationamong autonomous multimedia users,” IEEETrans. Circuits Syst. Video Technol., vol. 20,no. 2, pp. 297–309, Feb. 2010.[13] Y. Chen, et al., “Multiuser rate allocationgames for multimedia communications,”IEEE Trans. Multimedia, vol. 11, no. 6, pp.1170–1181, Oct. 2009.[14] Q. Zhang and G. Liu, “Rate allocationgames in multiuser multimediacommunications,” IET Commun., vol. 5, no.3, pp. 396–407, 2011.[15] H. Park and M. v. d. Schaar, “Bargainingstrategies for networked multimedia resourcemanagement,” IEEE Trans. Signal Process.,vol. 55, no. 7, pp. 3496–3511, Jul. 2007.[16] F. Kelly, “Charging and rate control forelastic traffic,” Eur. Trans. Telecommun., vol.8, no. 1, pp. 33–37, 1997.[17] A. Khan, et al, “Quality of experiencedrivenadaptation scheme for videoapplications over wireless networks,” IEThttp://committees.comsoc.org/mmc 11/22 Vol.4, No.4, August 2013
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