Novel AHP-based QoE Factors Selection Approach

Transcription

1 Novel AHP-bsed QoE Fctors Selection Approch Yosr Ben Youssef, Mériem Afif, Smi Tbbne MEDIATRON: Reserch Unit on Rdio Communiction nd Multimedi Networks Higher School of communiction of Tunisi (SupCom) Crthge University, Tunisi {yosr.benyoussef, mriem.fif, Abstrct Due to the ccelerting uptke of multimedi services over Internet, Qulity of Experience (QoE) ssessment hs become topic of prime importnce. While current QoE prediction models focus on technology-centered Qulity of Service (QoS) prmeters s the min influencing keys on user s perception qulity, they do not sufficiently ddress the following question: Bsed upon QoS, is it competent to understnd the overll QoE? Two limittions on QoE models hve been noticed: (1) lck of fctors introducing in ssessment models, nd (2) studies tht exmine the impct of ech fctors on QoE re limited. The need to tke more holistic view of influencing QoE fctors hs steered reserchers towrds n in-depth study on the multidimensionl spect of QoE. In order to tckle this chllenge nd giving the growing importnce of understnding QoE nd the fctors influencing it, our contribution in this pper is to give solution to get round limits mentioned bove. This pper proposes proper design of selection fctor bsed on the Anlytic Hierrchy Process (AHP) derived from Multi-Criteri Decision Mking (MCDM) theory. The result shows tht due to our model, we cn retrieve rnking of the influentil fctors. The relevnt gol of this pper is to give structured process tht exmines influencing fctors on QoE. Future QoE ssessment models cn benefit from pplying our proposed model in order to build QoE model s relistic s possible. Keywords Qulity of Experience; Qulity of Service; Influencing fctors. I. INTRODUCTION Video streming services hve now mjor shres over the Internet nd become dominnt in the globl trffic. The service providers need to be ble to rect upon qulity problems before the customer perceived them. Becuse of this trend, Qulity of Experience (QoE) hs recently gined greter ttention from cdemic reserch. In order to meet higher user expecttions, it becomes crucil to devise strtegy to mesure qulity of experiences. Aginst this bckground, severl studies re shifting towrd humn-centric pproch by developing QoS/QoE correltion models [1]. By nlysing these models, we deduce, on the one hnd, tht in order to estimte the perceived qulity of video services, its keep nerest to network QoS s the min influentil fctors on QoE. On the other hnd, integrting these technicl fctors is limited to one or two prmeters [2]. To ddress these limittions in QoE modelling, we believe tht it s chllenging to investigte lrge number of influencing fctors thoroughly in order to find out its degree of impct on user stisfction such s diverse conditions of video content nd mobile devices. In this work, we motivte novel pproch to the following objectives: Select lrge number of fctors s relistic s possible. Deduce degree of importnce of such fctors on user s perception. As solution, we propose selection pproch bsed on widely ccepted methodology for decision-mking: The Anlytic Hierrchy Process (AHP) which will be briefly reviewed in Section III. Owing to its simplicity to resolve such problem of decision, AHP hs found redy cceptnce by decision mkers in vrious fields such s mngement nd mrketing. As stted before, the gol of this work is to further exmine influencing fctors on QoE in order to meet the higher expecttion of qulity of experience ssessments. The reminder of this pper is orgnized s follows. Section II dels with the relted work. Section III develops our proposed model followed by discussion of our contribution in Section IV. Finlly, Section V is dedicted to our conclusion. II. RELATED WORKS With the uptke of reserch in QoE domin, it becomes crucil to extend the investigtion nd explore the humn side of QoE. Bsed solely on QoS s the mjor influencing fctor on qulity perception gives feeble results. Authors in [3] expose the impct of QoS typiclly specified in technicl terms, s n influentil fctor. Results show tht with severe degrdtion pf video qulity (pr consequent the degrdtion of QoS), the user my not perceive significnt loss of informtion content. Authors in [4] explin tht the high QoS observed will not gurntee user stisfction. In this context, QoS is used s guideline of cceptnce qulity nd considered s subset of the overll QoE. The need to tke more holistic view of influencing QoE fctors hs steered reserchers towrds n in-depth study on the multidimensionl spect of QoE. This concept will extend the trditionl understnding of QoE bsed only on networkrelted prmeters. In tht direction, uthors in [5] del with this orienttion by introducing two side of influencing fctors reserch. The first /16/$ IEEE

2 side concerns network-relted prmeters nd the second dels with user-centric prmeters. In [6], it proposes two types of fctors: technicl nd non-technicl. Authors in [4] proposes five dimensions of influencing fctors : 1) technicl prmeters cover QoS metrics, 2) usbility tht encompsses users feelings, 3) subjective evlution includes device chrcteristics, 4) expecttions which re relted to user need nd 5) Context s n externl fctor tht include environmentl nd socil spects. Due to the multi-dimensionl nture of QoE, it is therefore impossible to rely on objectives fctors (networks fctors) lone when defining perceived qulity. And to design complete QoE ssessment model, we should involve more influencing fctors on user experience. Recently, the selection of fctors, to tke into considertion for estimte the overll QoE, differs from one reserch to nother. Ech reserch evoques fctors in ctegory form. Authors in [7] expose onwrd the expectble re in which different influencing fctors re presented. These fctors re defined ccording to three min ctegories. Network conditions including QoS prmeters, device prmeters include device resolutions nd video prmeters include video codecs. In [8], the cceptnce qulity threshold hs been determined through three directions of influencing fctors: 1) Medi-level fctors includes compressing cpbility of codecs, 2) network-level side includes QoS prmeters nd 3) Contentlevel prt includes temporl nd sptil resolution. Authors in [9] nd [10], sme fctor s clssifiction ws proposed. Fctors re ctegorized into device-relted, video-coding relted nd content-relted. The studies, previously mentioned, proposed different clssifictions of fctors tht hve n impct on user s perception. Despite the diversity of explortion nd exposure of these fctors, the study of the impct of such fctors is minly bsed on subjective test where users re involved to rte the perceived qulity of video. According to these tests, correltion study between scores given by the users nd the fctor is done. Simultions cn give different results from one study to nother. Indeed, if we tke the resolution s fctor tken into ccount in study, this fctor ccording to [7] hs more influence on QoE thn frme rte. While frme rte in [10] is considered s the min influence fctor ffecting multimedi bndwidth requirement nd therefore users perception is significntly ffected between different frme rtes (5, 15 nd 25 fps). According to these studies, we conclude some remrks: The selection of fctors remins nrrow. The decision of the most influentil fctor depends on the simultion s condition. We believe tht, the selection of fctors tht hve the hevy influence on user s perception is frequently used but seldom defined into structure wy with sufficient clrity nd precision. As result, it s importnt to study the trde-off between fctors nd their influence on QoE. The investigtion tht hs been done llow us to get first insight into the impct of different fctors tht my hve influence on QoE. III. AHP OVERVIEW It s obvious tht integrting fctors others thn networkrelted is fundmentl step to build QoE ssessment model. Becuse of the need to structure the choice of the influentil fctors on QoE, our contribution is to model our reserch problem into process tht cn give n order of importnce of ech fctors ccording to one objective : their impct on qulity of experience. In this direction, how should decision mker select from given set of competing lterntives (fctors) tht re evluted through multiple criteri. We propose to use the Anlytic Hierrchy Process (AHP) decision support system derived from the Multi-Criteri Decision Mking (MCDM) theory. The Anlytic Hierrchy Process [11] ws developed by Thoms L. Sty nd it is populr MCDM tht gives solution of multi-criteri with finite number of lterntives to be put in rnk order by priority. A. AHP principle The AHP is theory of mesurement bsed on reciprocl mtrix of decision obtined by the method of pirwise comprisons nd tke into considertion the judgments of experts to derive priority scles (weight priority). The gol of such decision support is to rech the pproprite lterntive out of rnge of given lterntives in order to solve decision problem. This pproch ws the workhorse for resolving such problem by involving qulittive dt nd hs been pplied in vrious settings of decision mking. AHP hs been used to give n ssistnce to numerous decision mkers like choosing product mrketing strtegy or determining the best reloction site for the erthquke devstted Turkish city in 2001 [11],[12]. Before modeling this pproch in our reserch problem, we briefly review the AHP procedure which is fundmentl for this work. B. AHP procedures The methodology of the AHP cn be explined in the following steps: Step 1: Modelling the hierrchy Step 2: Judgements of the elements Step 3: Clcultion of the weights of the entire elements Step 4: Anlyse the results 1) Modelling the hierrchy: To mke decision in n orgnized wy to generte priorities; we need to structure the decision hierrchiclly from the gol of the decision in which we stte the objective, through the intermedite level in which we define criteri to the lowest level with the rnge of lterntives (Fig. 1).

3 TABLE III EXAMPLE OF PAIRWISE COMPARISON ON THE CRITERIA Criteri Criteri C1 C2 C3 Weights C1 1 1/7 1/ C C3 3 1/ Fig. 1. A hierrchy structure of AHP 2) Judgements of the criteri The AHP cn show the importnce of the defined criteri regrding their impct on the decision. Once different informtion (criteri nd lterntives) ws defined, the reltive importnce of the criteri must be determined. Therefore, qulittive nd quntittive criteri will be pir-wise compred using judgments to determine the finl rnking. Experts cn rte the comprison for ech criterion s equl, mrginlly strong, strong, very strong, nd extremely strong nd converted into quntittive numbers. Tble I shows the fundmentl scle of grding which corresponds to set of numbers tht indictes how mny times dominnt one criterion is over nother criterion. 3) Clcultion of the weights in ech level of the hierrchy After hving obtined rnk of priorities from pirwise mtrix, T. Sty proposes solution to determine the reltive importnce of the criteri using the eigenvector. And then, we obtin criteri s weights (Tble II). In the next step, the lterntives which represent the different choices of ction vilble to the decision mker, need to be nlysed nd prioritized with the respect to the multiple criterion with which the MCDM problems re ssocited. TABLE I. FUNDAMENTAL SCALE OF ABSOLUTE NUMBERS OF AHP [11] Intensity of importnce Definition 1 Equl Importnce 2 Wek or slight 3 Moderte importnce 4 Moderte plus 5 Strong importnce 6 Strong plus Demonstrted 7 importnce 8 Very, very strong 9 Extreme importnce Explntion Two ctivities contribute eqully to the objective Experience nd judgement slightly fvor one ctivity over nother Experience nd judgement strongly fvor one ctivity over nother An ctivity is fvored very strongly over nother The evidence fvoring one ctivity over nother is of the highest possible order of ffirmtion We then define pir-wise comprison tht determines the preference of ech lterntive over nother ccording to ech criterion. Let us denote the lterntives by A1, A2 An (n numbers of compred lterntives), the Mtrix [ ] represent the intensities of the decision preference between individuls pirs of lterntives (eq. 1). Where is the vlue of the th lterntive in terms of the th criterion. A= M i M i2 L L O L 1j 2j M ij 4) Anlyse the results The lst prt of AHP process clled synthesis involves getting the finl priority of lterntives. The dditive weighting method (eq. 2) is used to clculte the utility of ech lterntive cross ll criteri. We need, therefore, to multiply ech lterntive s rnkings by the priority of its criterion. This product is summed over ll the criteri for the prticulr lterntive to generte the rnk of the lterntive. S N i = j=1 ij w Where S i= the rnk of the i th lterntive, ij= the ctul vlue of the th lterntive in terms of the th criterion nd w j= the weight of the j th criterion. We illustrte the finl result of the decision in Fig.2 where the first lterntive A1 is chosen ccording to its obtined score S 1higher thn S2score of the lterntive A2. Fig. 2. Illustrtion of decision mking bsed on AHP j (1) (2)

4 IV. OUR PROPOSED MODEL A. Motivtion In order to build such model, some works hve focused on finding generic reltionships between QoS problems nd their impct on QoE problems. Authors in [2] proposes generic formul bsed on n exponentil interdependency of QoE nd QoS (eq. 3) β QoE = α e QoS + γ (3) The obtined model is retrieved by mens of nonliner regression where the QoE outcome is depending on the inserted QoS vlue. We then wnt to notice tht the pcket loss rtio is the only one impirment fctor indicting QoS problems. Hence, QoE result chnges with the respect to one fctor t the sme time. As result, we cnnot drw generic conclusion on user perception from this proposed pproch. Authors in [9] proposes n A-QoE model bsed on fiveprmeter logistic function (eq. 4). QoE d + x b ( 1+ ( ) ) c = (4) The outcome mesured the generl cceptbility of qulity s proposed QoE mesure. As the input of the model, video coding prmeters nd content identifiction chrcteristics re considered s the influencing predictors. In this exmple, more thn one prmeter representing fctors is tking into considertion. Hence, it gets round the fct tht one prmeter is not enough to deduce generic QoE result. However this model reches nother side of fctors, it neglects the impct of QoS s prt of QoE predictors. Aside the fct tht these correltion models refer only on specific side of fctors nd neglect other one, we believe tht it becomes crucil to give structured wy tht study in depth influencing fctors on QoE. Our proposed model gives solution to get round two prts: 1) Considering lrge number of fctors in ddition to network-relted ones. 2) Exmining the influence s degree of fctors in the user s perception. B. Modelling Among plethor of fctors tht my influence QoE, our gol is to find the dequte order corresponding to the most influentil fctors. We need then to pick the lterntives (fctors in our cse) s the input of the AHP process. Aginst this bckground, we mke decision to select fctors from mong three ctegories. The first ctegory is the network relted fctors which represents QoS prmeters. We select pcket loss nd dely s the prmeters tht my hve strong influence on user (dis)stisfction. In ddition to network prmeters, externl fctors hve gret impct on user perceived qulity Therefore, the second e ctegory of selection will represent displying device-relted fctors nd then we select Rtio of Video frme resolution to Device resolution (RVD). We focused on this prmeter becuse it includes two fctors simultneously: the imge resolution nd the device displying resolution. The results of such prmeters show in [9] tht RVD hs n impct on plesnt cceptbility. The third ctegory of the criteri s selection is video coding prmeters tht include frme rte (FR) [13]. This metric rouse the interest of mny QoE studies nd it shows tht FR hs n impct on perceived qulity [7, 9 nd 10]. To study nd nlyze the influence of these exmples of fctors through AHP process, the decision mong these lterntives need to be tken on the bsis of multiple criteri rther thn single criterion. Aginst this bckground, the chosen criteri re bsed on the different wy tht mesure QoE. Therefore, we select the criteri tht evlute the qulity of multimedi contents. The criteri selected to be integrted into our model re the following: Men Opinion Score (MOS) [14]: it is widely used exmple of subjective mesurement method, set forth in ITU-T recommendtion [15], in which users rte the video qulity by giving five different point score from 5 to 1, where 5 indictes excellent perceived qulity nd 1 is equl to the worst qulity. Acceptbility score (ACC): Although, the MOS score is commonly used, it hs been criticized to not give sufficient results of user s perception becuse its lcks of cceptnce threshold detil. Thus, uthors in [16] proposes n cceptnce metric s binry mesure to give the overll cceptnce of qulity level. Stndrd devition of Opinion Score (SOS): is mesure tht gives n dditionl informtion to MOS by tking into considertion the diversity of user stisfction rtings [17]. Video Qulity Metric (VQM) [18]: It s n objective mesure of QoE tht estimte the performnce of video streming service without involving humn. It is rnged from 0 to 1. The lower VQM scores correspond to the higher subjective qulity. Modeling the AHP process to solve our reserch gp ws performed in MATLAB, the following illustrtion (Fig. 3) shows our proposed solution. Fig. 3. Illustrtion of our proposed model bsed on AHP process.

5 As result, we deduce in Tble II the pirwise comprison of the criteri. The judgments concerned the criteri re bsed on the degree of importnce of the qulity of experience s mesures nd their relevnce. Indeed, MOS remins most importnt result tht the VQM since it is n estimtion of finl results given by the subjective mesure. In ddition, cceptbility score nd Stndrd devition of Opinion Score SOS re considered s worn s contribution on MOS limits. Indeed, ACC gives cler nswer on the cceptbility of qulity of service then mesure of the MOS. If we receive MOS tht vries between 2 nd 3 this hs resulted in poor perceived qulity despite the qulity did not relly deteriorted nd still cceptble. Thus, judgements slightly fvored MOS nd ACC on VQM nd fvored strongly to both ACC nd SOS results. The lst column in Tble III represents the result of weights given by the eigenvector in order to obtin the importnce of ech criterion. TABLE IIII. PAIRWISE COMPARISON ON THE FOUR CRITERIA MOS ACC VQM SOS Weights MOS 1 1/5 3 1/ ACC / VQM 1/3 1/3 1 1/ SOS h 1 Performing the synthesis prt of the AHP process, Tble III shows the resulting pir-wise comprison mtrix tht represents the weights of lterntives with respect to ech criterion. The lst column gives the rnk of ech lterntives given by the weighted-sum method. Altern tives TABLE IV. AHP RESULTS OBTAINED IN OUR MODEL Criteri MOS VQM ACC SOS Score Pcket loss Dely RVD Frme Rte From this result, we conclude, ccording to the order rnk of influencing fctors tht pcket loss prmeter is the dominting fctor ffecting QoE followed by dely nd RVD nd FR hve the sme influence on perceived qulity. To give synthesis result, we conduct the AHP pproch on severl itertions to relese generl trend of influencing fctors result. Fig 3 shows tht the order of importnce of ech fctors suggest pcket loss s the most importnt one. 23,78% 33,33% 21,78% Pcket loss Dely RVD FrmeRte 21,11% Fig. 4. Pie chrt representtion of the rnking order corresponding to the most influentil fctor. The fctors nd criteri considered s the input of AHP model re none other thn to test the fesibility of our model nd to introduce the ide of using decision mking support system in the context of QoE study We believe tht bsed on our model, n in-depth study on system s inputs should be performed in order to give complete result to build QoE ssessment model. Otherwise, our contribution leds to tke into considertion the following possibilities: Considering n exhustive list of fctors including technicl nd non-technicl one s the model s input. Selecting the criteri ccording to pre-study on the impct of ech criterion on the fctors. Our model llows future studies to mnge with ll these fctors nd investigte their importnce on QoE in structured nd unified wy. V. CONCLUSIONS With the introduction of QoE concept, there is need to investigte fctors influencing user s perception. In this regrd, we propose selection pproch of fctors bsed on the Anlytic Hierrchy Process AHP. A widely used decisionmking pproch. The im of our contribution is to give process tht justifies the choice of fctors to integrte in QoE ssessment model. Aginst this bckground, the min ide in our contribution is to introduce multi-criteri decision notion to resolve selection problem in QoE context. As perspectives, we believe tht, bsed on our proposed model, we will include lrge number of fctors tht my hve hevy impct on user (dis)stisfction in order to design generic decision process which responds to compromise between integrting utmost fctors nd generting significnt QoE model.

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