A Novel Cooperative Content Fetching-based Strategy to Increase the Quality of Video Delivery to Mobile Users in Wireless Networks

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1 1 A Novel Cooperatve Content Fetchng-based Strategy to Increase the Qualty of Vdeo Delvery to Moble Users n Wreless Networks She Ja, Changqao Xu, Member, IEEE, Janfeng Guan, Hongke Zhang and Gabrel-Mro Muntean, Member, IEEE Abstract Cooperatvely fetchng vdeo content wth the help of other moble nodes can release some of the pressure on storage and bandwdth of the constraned moble devces n wreless networks and speeds up the localzaton process of vdeo resources so as to support better qualty of vewng experence. In ths context, the dscovery of the approprate moble cooperatve node becomes a key factor and s also a challenge for the deployment of such a fetchng scheme. In ths paper, we ntroduce a novel cooperatve content fetchng-based strategy to ncrease the qualty of vdeo delvery to moble users n wreless networks (CCF). By ntellgently montorng the real-tme varaton n the state of the one-hop neghbors (mmedate-neghbors) of the vdeo resource downloader, CCF employs an nnovatve estmaton model to measure the stablty of these mmedate-neghbors. In order to enhance the cooperatve fetchng effcency, CCF desgns a communcaton qualty forecast model to measure lnk relablty and forecast the avalable bandwdth. By makng use of a newly proposed cooperatve fetchng algorthm, CCF can speed up fetchng and dssemnatng of vdeo resources wth the help of cooperatve neghbors selected n terms of stablty and communcaton qualty. Smulaton results show how CCF obtans hgher selecton accuracy of cooperatve neghbors, lower average end-to-end delay, lower average packet loss rato, hgher average throughput, hgher vdeo qualty and lower mantenance overhead n comparson wth state of the art solutons. Index Terms Stablty, Vdeo Delvery, Cooperatve Fetchng, Wreless Networks, Vdeo Qualty. I. I NTRODUCTION T HE latest developments n moble and wreless networks have fueled an mpressve growth n type and number of Ths work was supported n part by the Natonal Natural Scence Foundaton of Chna (NSFC) under Grant and Grant , n part by the Fundamental Research Funds for the Central Unverstes, n part by the Natonal Key Basc Research Program of Chna (973 Program) under Grant 213CB32912, n part by the Beng Natural Scence Foundaton under Grant , and n part by Scence Foundaton Ireland under the Internatonal Strategc Cooperaton Award Grant SFI/13/ISCA/2845. S. Ja s wth the State Key Laboratory of Networkng and Swtchng Technology, Beng Unversty of Posts and Telecommuncatons, Beng 1876, Chna. He s also wth the Academy of Informaton Technology, Luoyang Normal Unversty, Luoyang 47122, Henan, Chna (e-mal: sha@gmal.com). C. Xu and J. Guan are wth the State Key Laboratory of Networkng and Swtchng Technology, Beng Unversty of Posts and Telecommuncatons, Beng 1876, Chna (e-mal: {cqxu,fguan}@bupt.edu.cn). H. Zhang s wth the Natonal Engneerng Laboratory for Next Generaton Internet Interconnecton Devces, Beng Jaotong Unversty, Beng 144, Chna (e-mal: hkzhang@bupt.edu.cn). G.-M. Muntean s wth the Performance Engneerng Laboratory, School of Electronc Engneerng, Network Innovatons Centre, Rnce Insttute, Dubln Cty Unversty, Dubln 9, Ireland (e-mal: gabrel.muntean@dcu.e). Fg. 1. Example of one-hop neghbor cooperatve fetchng applcatons n commercal, entertanment, mltary, and educatonal areas [1]. Among these, provdng rch meda content to moble users s ncreasngly popular and was responsble for over one-thrd of all consumer network traffc [2]-[5]. New multmeda applcatons lke 3D stereo vdeo have become the rreversble trend and trgger hgher bandwdth demand than tradtonal streamng servce [6]-[9]. However, supportng effcent hgh qualty real-tme vdeo delvery to moble users n wreless networks s very challengng mostly due to moble devce and network constrants n terms of bandwdth, storage and lnk relablty, especally n wreless mult-hop envronments [1]-[13]. Unfortunately, user perceved qualty for multmeda servces delvered over these networks s n general low. Recently, cooperaton has become the underlyng gudng prncple n numerous (meda) resource fetchng research efforts [14]-[24]. For nstance, nformaton-centrc networkng (ICN) s a clean-slate networkng archtecture that puts nformaton n focus nstead of the nterconnecton of specfc nodes so as to acheve effcent and relable dstrbuton of resources [25]. ICN archtectures leverage n-network collaboratve storage for cachng, multparty communcaton through replcaton and nteracton models n terms of nterest and dstrbuton for resources. ICN accelerates the dssemnaton

2 2 and sharng of resources and helps where there s shortage of avalable resources. However, none of these approaches nvolvng management, retreval and dstrbuton of vdeo resources can support real-tme multmeda streamng servce n moble wreless networks. Moreover, the churn for replacng resources results n wastng the bandwdth and storage and ncreasng the energy consumpton of the moble nodes. Makng cooperatve use of the storage space and bandwdth of the nodes geographcally close to the downloader (e.g. one-hop neghbors) to fetch meda resources, not only meets the real-tme requrements of meda streamng servces, but also speeds up the download process, mproves resource sharng capacty and overcomes the negatve nfluence of the frequent lnk breaks n wreless mult-hop communcatons. A number of proposals have exploted the beneft of cooperaton among moble nodes whch have close geographcal locaton [14][19][22][24]. The performance of the cooperaton mode reles on the selecton of cooperatve nodes. The moblty of moble nodes leads to dynamc varaton of geographcal locaton and communcaton qualty between the two cooperatve partes, reducng the effcency of meda resource delvery. However, there are some defcences n the selecton of the cooperatve nodes n the exstng studes. For nstance, some related resource sharng algorthms rely on the supposton that the cooperatve partes have close geographcal locaton durng a certan perod tme [14] or neglect the moblty of moble nodes [22]. Therefore, an effcent soluton based on sensng moblty varaton of moble nodes and communcaton qualty n the transmsson path of meda content should be consdered for cooperatve fetchng strateges n wreless networks. In ths paper, we propose a novel Cooperatve Content Fetchng-based strategy to ncrease the qualty of vdeo delvery to moble users n wreless networks (CCF). As Fg. 1 shows, the path of cooperatve fetchng vdeo content ncludes two parts: from the downloader to the cooperatve node (CN) (.e. from the downloader to node B) and from the cooperatve node to the content suppler (.e. from node B to the server). CCF reles on an nnovatve stablty estmaton model (SEM) and a novel communcaton qualty forecast model (CQFM) to select the cooperatve node from onehop neghbors and mprove the performance of cooperatve fetchng of vdeo content. SEM evaluates the moblty stablty of the one-hop neghbors n terms of the moblty volatlty, n order to keep a stable one-hop neghbor relatonshp between the downloader and selected CN and obtan hgh transmsson effcency n the path from the downloader to CN. CQFM measures lnk relablty and forecasts avalable bandwdth of the one-hop neghbors n the whole path of cooperatve fetchng, whch enhances the delvery capacty of vdeo data. For each downloader, CCF consders the one-hop neghbor whch has the more stable one-hop neghbor relatonshp wth the downloader and the hgher and more relable forecasted bandwdth as a CN, whch ensures the effcency of cooperatve fetchng. CN-asssted fetchng algorthm shortens the process of vdeo resource localzaton and avods the use of mult-hop lnks n order to ncrease the qualty of vewng experence. Extensve tests show how CCF acheves much better performance results n comparson wth other state of the art solutons. II. RELATED WORKS There have been numerous recent studes on cooperatve fetchng. Tu et al. proposed a collaboratve content fetchng scheme for groups of moble subscrbers wth common characterstcs (C5) [14]. C5 makes use of a small scale P2SP framework n a hybrd moble network to maxmze the utlty of WWAN lnks n order to cope wth concurrent moble Internet traffc. The nodes n close vcnty whch use the hgh-speed WLAN form a group whose members share ther own data wth other requestng members by makng use of multcast at MAC layer. C5 can speed up the fetchng rate for communty members by usng dle WLAN nterfaces for n-communty communcaton. However, C5 reles on the premse that a number of moble subscrbers are close to each other for a perod of tme and fetch the same content from Internet, whch s not true most of the tme. In MobTorrent [15], ndvdual vehcles use the WWAN rado (e.g. GPRS) to nform one (or multple) selected AP(s) to pre-fetch content. The pre-fetched data s then replcated to other carrer nodes (moble helpers) and propagated to the requestng vehcle. By makng use of cooperatve pre-fetchng, the vehcle breaks the constrants of the bandwdth to download large-sze fles n short perods of tme. However, MobTorrent depends on a necessary premse: moblty nformaton of vehcles can be predcted wth hgh accuracy usng the Automatc Vehcle Locaton (AVL) system and past hstory, whch s not always the case. Cruces et al. proposed a carrer selecton strategy based on contact maps and has deployed hs soluton n a vehcular space [18]. By analyzng hstorcal data of nter-vehcle communcaton, contact maps among vehcles are bult. Contact maps can be exploted by APs n the cooperatve download process, by estmatng the meetng probablty between downloader and canddate data carrers n order to select most promsng local vehcles as data carrers. However, by makng use of AP-to-AP and AP-to-vehcle nformaton exchange to estmate the meetng probablty of two vehcles, the overhead of nformaton exchange, startup delay and probablty of falure n the carrer selecton strategy ncrease and consequently the soluton wll not meet the realtme demands of meda streamng servces. Malandrno et al. proposed a soluton to enhance the AP deployment to optmze the performance of vehcular cooperatve download n urban scenaros, by addressng channel contenton and data transfer paradgm [2]. However, the soluton needs to have perfect knowledge of vehcle traectores and schedule of data transmssons. Furthermore, the mult-hop traffc delvery between the downloader and cooperatve node (other vehcle or AP) s subected to long delays and frequent lnk breaks. Raveendran et al. proposed a moble multpath cooperatve network for real-tme multmeda servces [19]. A moble devce can access the cloud usng multple access lnks provded by ts neghborng moble devces. By dynamcally establshng multple paths among neghborng cooperatve devces over WWAN and WLAN, the provder of vdeo resources uses the dstnct end-to-end paths to delver the streamng data

3 3 to the requester. The combned capacty of multple wreless access lnks enable support for hgher throughput so as to guarantee ncreased user perceved qualty for the multmeda servce. However, ths soluton neglects the nvestgaton of moblty for neghborng cooperatve devces. The movement of cooperatve devces leads to the unstable lnks and declnng qualty levels for the multmeda content delvery, so the performance of multpath transmsson s negatvely nfluenced. PatchPeer proposed n [22] uses a Closest Peer scheme to select the patchng peer wth the closest Eucldean dstance from ts one-hop neghbors to receve patch meda streamng data. The patch streamng servce provded by the closest peer supports contnuous hgh qualty servce provsonng wth low delay and reduced packet loss rato (PLR). However, PatchPeer neglects the fact that the node moblty ncurs and the dstance between the ntal closest peer and requester changes n tme. If the closest peer becomes a mult-hop neghbor nstead of a one-hop neghbor, the qualty of the meda streamng servce quckly declnes. Ahlgren et al. proposed a cooperatve cachng approach n moble ad-hoc networks (COCA) [23]. COCA groups the moble nodes n MANETs nto several clusters where the members n clusters have specfed roles and autonomously mantan the cluster structure. The moble nodes mplement cooperatve cachng n the process of forwardng data and flexbly manage the cached content (e.g. removng obsolete content and cachng new data). COCA uses a herarchcal searchng strategy to dssemnate the resource request messages accordng to the prorty sequence: local, neghbors and data center. COCA takes full advantage of the storage space of each moble node to ncrease the effcency of resource searchng n moble ad-hoc networks. However, the cluster head needs to respond the request message and mantan the cluster members. The cluster head cannot support the hgh load due to the capacty constrants of the moble devce n terms of storage, computng and energy. The cachng and replacement of data can further consume the lmted energy. Therefore, COCA s dffcult to adapt to the moble envronment. Moreover, the proposed request dssemnaton algorthm reles on message broadcast, whch ncreases the network load, wastes network bandwdth and nvolves hgh delays. Hao et al. proposed a secure cooperatve data downloadng framework for pad servces n vehcular ad hoc networks [24]. The vehcles make use of a coordnaton channel to broadcast one-hop request messages. The neghbor nodes of requester forward the request message to a road sde unt (RSU). After recevng the request message, several vehcles whch have relatve close geographcal dstance wth the requester are assgned to download the specfed data unt from RSU and forward t to the requester. The cooperatve download approach can facltate data downloadng and avod the hdden termnal ssue by coordnatng the relay transmsson. However, the proposed soluton focuses on non-real tme data downloadng n a hghway scenaro only. The complex moblty of vehcles brngs negatve effects on the effcency of downloadng and sharng of data n a non-lnear urban envronment. Moreover, the hgh coordnaton complexty between multple cooperatve nodes cannot ensure real-tme delvery of data. The stablty of the one-hop neghbor relatonshp between downloader and cooperatve node and the of communcaton qualty level play the mportant role n cooperatve fetchng algorthm. Ths s due to the fact that they have a drect effect on vewer perceved qualty. III. CCF DETAILED DESIGN For convenence, Table I defnes several notatons whch are used n ths secton and the followng ones. Assume each moble devce s equpped wth a GPS recever, a wreless network nterface and a vdeo player. As llustrated n Fg. 1, the wreless sgnal range of each moble node N (potental downloader ), defnes ts Immedate Neghbor Regon (IN- R), smlar to [26]. Each moble node n, one-hop neghbor n N s INR (e.g., A, B, C, D, E and F) s consdered N s mmedate-neghbor (IN) [27]. By makng use of GPS recever-based locaton-awareness as descrbed n [26][28], N can obtan the geographc poston of all ts IN nodes n INR. N s wreless network nterface supports the transmsson of multmeda data and exchanges control messages between N and ts IN nodes. N makes use of one-hop multcast at MAC layer to detect the state of each of ts IN nodes. These IN nodes respond wth messages contanng ther average movng speed. N uses a lst to store the nformaton related to ts IN nodes, as follows: NL (n 1, n 2,..., n n ) s the IN lst of N, where n c ((X c, Y c ), sp c ) s a 2-tuple contanng the coordnates of the geographc poston and the average movng speed of node n c, IN of N, lsted n NL. Along wth the movement of moble nodes, the relatonshp between the geographc postons of the IN nodes dynamcally changes, and therefore the membershp of NL set vares. By reusng the locaton-awareness approach and sendng the detecton message at regular tme ntervals, N obtans new IN set data NL (new) (n 1, n 2,..., n m ). All tems n the fnte set S = (NL (new) NL ) = (n a, n b,..., n v ) are consdered as CN canddates, where the length of S s v. N manages the IN nodes n terms of ther onng, leavng or stayng n the INR. If an IN node n leaves/enters N s INR, N removes/adds n s nformaton from/to NL. In order to dscover CN from the IN nodes, N assesses the moblty stablty n INR and communcaton qualty of all tems of S accordng to CCF s SEM and CQFM algorthms. Consequently, CCF can be consdered as a self-aware cogntve loop process [29], as llustrated n Fg. 2. (1) SEM: moblty stablty estmaton of IN nodes. For all the tems n S, N nvestgates node moblty based on the spatal dstance and average movng speed relatve to N (the relatve dstance and movng speed of IN nodes from N are consdered as estmaton parameters of movement state of IN nodes). N estmates the moblty volatlty of all the tems n S n current and prevous update rounds n order to be aware of the varaton levels of movement state of the IN nodes (sensng moblty volatlty of INs). N dynamcally regulates the perod tme of updatng the IN nodes n terms of estmated moblty volatlty (calculate update perod tme). The update perod tme s used to obtan the statstcal nformaton of

4 4 tme spent n N s INR (expressed n seconds) whch s a key parameter for the estmaton of moblty stablty of the IN nodes. N uses the movement drecton and varaton level of spatal dstance and average movng speed from N of each tem n S to calculate the weghted tme spent n N s INR. These values are consdered node s stablty values (estmate stablty of INs). Notatons N NL sp c NL (new) S v n G(N ) E(S (c) ) I(S (p) ) n sd n sp D(S (c) ) r D(S(c) ) d D(S(c) ) N l b l ut ut ( P ) R DR st TABLE I NOTATIONS USED BY CCF Descrptons a moble node n a wreless network IN lst of N average movng speed of moble node n c updated IN lst of N ntersecton of NL and NL (new) length of S a IN of N estmate value of moblty volatlty of INs n N moblty estmate value of INs n the current update round moblty estmate value of INs n the prevous update round spatal dstance between n and N relatve average movng speed between n and N dstrbuton of relatve average movng speed and spatal dstance between INs and N correlaton coeffcent of movement state of INs n N vertcal dstance from orgn mapped by N to fttng lne generated by D(S (c) ) n plane of relatve average movng speed and spatal dstance slope of fttng lne of D(S (c) ) s lnear regresson duraton of the next update round of N duraton of the current update round of N proporton of sample sze of moblty stablty estmaton angle cosne of movement vector of N and n estmate value of n s stablty n the current update round st (P ) estmate value of n s stablty n the prevous update round INS N s IN subset whose tems have a stablty value greater than B (O) x (t k) estmate value of bandwdth between n and n x n t k AB (O) x orgnal seres of bandwdth between n and n x B (A) x (t k) accumulated value of bandwdth between n and n x n t k AB (A) x accumulated seres of bandwdth between n and n x a Grey level of development u Grey level of control C x (v) resdual value between B (O) x (tv) and ˆB x (v) C x resdual mean value F x resdual varance R (P ) x posteror varance rato T H (R) a threshold value for measurng R (P ) x T H (P ) a threshold value for measurng P (P ) x INX N s IN subset whose tems have relable lnk state n the path from the IN nodes to vdeo content suppler INI N s IN subset whose tems have relable lnk state n the path from the IN nodes to N Fg. 2. The cogntve loop of CCF (2) CQFM: communcaton qualty estmaton of IN nodes. N nvestgates the bandwdth of the vdeo delvery path for all the tems n S. The delvery path from the suppler of vdeo resources to N s dvded nto two parts: N to CN and CN to suppler. N selects ts relatvely hgh stablty IN nodes to estmate the communcaton qualty (.e. lnk relablty and bandwdth forecastng value) n ther paths of cooperatve fetchng (path communcaton qualty of INs). N flters the IN nodes wth unrelable lnks (measure lnk relablty) and forecasts the bandwdth of the vdeo delvery path of the IN nodes wth relable lnks (forecast path bandwdth). N selects an IN node wth both relatvely hgh stablty and good communcaton qualty n the vdeo delvery path as the CN to help fetch the vdeo resources (select cooperatve neghbor). Once the CN contacts the suppler and receves the vdeo streamng data, the data s forwarded to N. Therefore, CCF adapts to the dynamc envronment by estmatng the moblty and communcaton qualty of IN nodes (adaptaton). In order to best desgn SEM and CQFM, CCF needs to address the followng four key problems: (1) mmedate neghbors volatlty estmaton. The nodes n S are consdered stable IN nodes, as they have experenced one or multple update perod(s) of N. We nvestgate node moblty level based on the volatlty of spatal dstance and average movng speed relatve to N for all tems n S. (2) IN lst membershp update. CCF employs a varable update perod scheme to mplement the mantenance of the IN lsts. In terms of IN nodes volatlty estmaton, N can tmely adust the update perod. (3) weght-based membershp stablty estmaton. The nfluence of node moblty on stablty estmaton s consdered. Varatons of speed, drecton and spatal dstance from N for each IN node n S are used as weght factors to calculate the stablty value of moblty. (4) communcaton qualty forecast. The selected IN nodes perodcally measure the avalable bandwdth to suppler. By nvestgatng the varaton level of the bandwdth, N estmates lnk relablty and forecasts the avalable bandwdth for these IN nodes. A. Immedate Neghbors Volatlty The motvaton for the estmaton of any node N s Immedate Neghbors Volatlty s to dscover the varaton level of moblty for all IN nodes n S n order to nfluence the update

5 perod for the IN lst. Inspred by the Informaton Theory model [3], we use the nformaton content G(N ) to ndcate the moblty varaton as n eq. (1). G(N ) = E(S (c) ) I(S (p) ), G(N ) [ 1, 1] (1) where S (c) and S (p) are the current and prevous moblty of IN nodes n S for the update perod at N, respectvely. E(S (c) ) and I(S (p) ) are nformaton entropy generated by S (c) and S (p), respectvely and ther values are obtaned from eq. (2) and eq. (12), respectvely. P (c) log 2 P (c) <P (c) < 1 E(S (c) ) = P = 1 P (c) = 1 P (c) ndcates the estmaton value for moblty of all tems n S (c). By nvestgatng the moblty dstrbuton of all tems n S (c), we use the Least Square Method (LSM) [31] and the Lnear Regresson Fttng (LRF) [32] to calculate P (c). Let D(S (c) ) be the fnte set denotng the dstrbuton of moblty where each tem composed of 2-tuples stores the average movng speed and spatal dstance relatve to N for each IN tem n S (c), accordng to eq. (3). D(S (c) ) = {(n sda a, n spa a ), (n sd b b, n sp b b ),..., (n sd k k (2), n sp k k )} (3) where n sd and n sp are the spatal dstance and average movng speed relatve to N of IN node n S respectvely. n sd s ntal value can be obtaned from eq. (4). n sd = (X X ) 2 + (Y Y ) 2, n sd [, n sd MAX] (4) where (X, Y ) and (X, Y ) are the geographc poston coordnates of N and n, respectvely. n sd MAX s the maxmum dstance from N and ts value should be set to the radus R of INR (expressed n meters (m)). n sp s ntal value derves from the absolute value of the dfference between the average movng speed of N and n, accordng to eq. (5). { n sp N sp = n sp MAX n sp N sp N sp n sp < nsp MAX n sp nsp MAX In eq. (5) N sp and n sp are the average movng speeds of N and n, respectvely and n sp MAX s the maxmum value of the relatve movng speed defned by CCF (expressed n m/s). For convenence, each tem n D(S (c) ) needs to be normalzed as ndcated n eq. (6). ˆn sp = n sp n sp, ˆn sd MAX = nsd n sd MAX, ˆn sp, ˆn sd [, 1] (6) Assume N s the orgn pont of a coordnate system n whch abscssa x and ordnate y represent the dstance from N and the average movng speed relatve to N, respectvely, as shown n Fg. 3. The tems n the D(S (c) ) set can be mapped (5) Fg. 3. n sp MAX N y d n 6 D(S (c) ) N ->l n 4 n 1 Lnear regresson fttng model n 7 n 2 n 3 l n 5 n sd MAX nto ths coordnate system. l s the lnear regresson fttng lne for D(S (c) ) and d D(S(c) ) N l s the vertcal dstance from orgn to the fttng lne. We use LSM to calculate the correlaton coeffcent r D(S(c) ) and consder d D(S(c) ) N l and r D(S(c) ) as estmaton parameters for calculatng P (c) accordng to eq. (7). P (c) = r D(S(c)) (1 d D(S(c) ) N l ), P (c) [, 1] (7) In eq. (7) r D(S(c)) [, 1] s the correlaton coeffcent of the regresson data pont n D(S (c) ) whch ndcates the dfference level of the moblty (relatve spatal dstance and average movng speed) of all tems n S (c), accordng to eq. (8). The larger the value of r D(S(c)) s, the lower the moblty state dfference of all tems n S (c) s. r D(S(c)) = ˆn sp c c ˆn spc c ˆn sp 2 x ˆn sp ˆn sd c c ˆn sd 5 ˆn sd c c ˆn sd 2 (8) where ˆn sp and ˆn sd are the mean value of the average movng speed relatve to N and the dstance from N of all tems n S (c), respectvely. Ther values can be obtaned accordng to eq. (9). ˆn sp = d D(S(c) ) N l ˆn spc c k, ˆn sd = ˆn sdc c k, ˆn sp, ˆn sd [, 1] (9) denotes the devaton between the movng state of N and those of all the tems n S (c), accordng to eq. (1). The lower the value of d D(S(c) ) N l s, the lower the moblty dfference between N and the tems n S (c). d D(S(c) ) N l = b lˆn sp ˆn sd + c 1 + b 2 l, d D(S(c) ) N l [, 1] (1) In eq. (1) b l s the slope of the fttng lne of D(S (c) ) s lnear regresson, accordng to eq. (11).

6 6 b l = ˆn sp c c ˆn sd c c kˆn spˆn sd (ˆn sdc c ) 2 k(ˆn sd ) 2, b l [, + ] (11) Eq. (12) ntroduces the nformaton entropy generated by S (p). P (p) log 2 P (p) <P (p) < 1 I(S (p) ) = P = 1 P (p) = 1 P (p) = r D(S(p)) (1 d D(S(p) ) N l ), P (p) [, 1] (12) where the computatonal method of P (p) s the same wth that of P (c) and was already descrbed. The larger the value of G(N ) s, the lower the volatlty s, whch means the IN nodes of N tend to be relatvely stable. B. Self-regulated Perod-based IN Lst Update Mechansm As already mentoned, there s a need for a mechansm to manage the IN lst updates for each node. A tmer-based update scheme s employed whch enables each moble node update ts one-hop neghbor lst every T tme unts (e.g., T of the order of mnutes, up to 1 hour) [33]. The algorthm nvolves two update perod types: statc and varable. The statc update requres the moble termnal to send perodcally detecton messages to all ts one-hop neghbors. The update perod settng drectly nfluences both the accuracy and the cost of mantanng the IN lst. For nstance, a long update perod reduces the number of detecton messages and therefore the overhead, but some of the IN nodes mght have changed ther poston and the lst does not contan most up-to-date nformaton. Conversely, a short update perod ensures the nformaton on the IN nodes s receved n real-tme; however ths needs frequent exchange of detecton messages and results n overhead ncrease. In terms of consderng a varable update perod for each node N to manage ts IN lst n INR, ths paper proposes a Self-regulated Perod-based Immedate-neghbor Lst Update Mechansm (SPUM). SPUM adaptvely adusts the update perod n terms of IN nodes volatlty estmaton. In ths manner SPUM not only reduces the number of messages exchanged wth the IN nodes, but also fast dscovers CN canddates. For nstance, whenever there are severe state varaton of stable IN nodes n S, N should reduce ts IN lst update perod to obtan hgher amount of data, especally about the CN canddates. Ths enables faster estmaton of stablty varaton n the process of stable CN dscovery. Conversely, where the membershp of IN nodes n S s stable, N should ncrease the update perod to reduce the message overhead. Let ut be the varable nformaton update perod of N, wth an ntal value sv greater than. The update mechansm of ut s defned n eq. (13). ut = ut (P ) (1 + R sn(g(n ))) (13) In eq. (13) ut and ut (P ) are the current and prevous values for the update perod at node N, respectvely and G(N ) s the varaton level of movng state of IN nodes n S, as descrbed n eq. (1). R s a weghted value to nfluence G(N ), accordng to eq. (14). S R = NL (new), R [, 1] (14) In eq. (14) S and NL (new) represent the number of tems n S and NL (new), respectvely. R s n fact the proporton of sample sze ( S ) when calculatng G(N ) relatve to NL (new). The larger the value of R, the larger the level of regulatng s. ut (P ) C. Membershp Stablty Estmaton Model As we know, the longer IN nodes reman n INR, the hgher ther membershp stablty s. We use a weghted soluton to addtvely consder membershp tme n order to estmate the stablty of each IN node. The varaton levels of the movement speed and drecton and spatal dstance for each IN node n S before and after updatng INR are used as weght factors to calculate the membershp stablty of n. (1)weght factor for movement speed and spatal dstance from N. By makng use of the Eucldean dstance formula, the weght factor of n s speed and dstance from N s obtaned accordng to eq. (15). vr = (ˆn sp (ˆn sp ) ) 2 + (ˆn sd (ˆn sd ) ) 2 (15) where ˆn sp average speed relatve to N before and after updatng INR, respectvely. ˆn sd and (ˆn sp ) are normalzed values of n s and (ˆn sd ) are normalzed values of n s dstance from N before and after updatng INR, respectvely. The lower the value of vr [, 2] s, the more stable the movement state of n s. (2) weght factor for movement drecton. (X (C), Y (C) and (X (P ), Y (P ) ) are current and prevous geographc poston ) coordnates of n for an update perod at N, respectvely. (X (C), Y (C) ) and (X (P ), Y (P ) ) are current and prevous geographc poston coordnates of N durng the same update perod, respectvely. The movement traces of n and N generate two vectors n and N respectvely accordng to eq. (16). n = (X (C) X (P ), Y (C) Y (P ) ), N = (X (C) X (P ), Y (C) Y (P ) ) (16) We use the vector angle cosne of n and N to ndcate the drecton dfference level n the movement trace between n and N accordng to eq. (17). N n DR = cos θ =, cos θ [ 1, 1] (17) N n

7 7 If DR (, 1], the movement drecton of n s smlar or consstent wth that of N. If DR [ 1, ), n and N have the nverse movement drecton. If DR =, the movement drecton of n and N s orthogonal. The membershp stablty of the IN node n of N s obtaned accordng to eq. (18). { (P ) st st = + ut cos(vr ) DR P D INR > P D INR = (18) where st and st (P ) (st, st (P ) (, )) are the membershp stablty of n an IN node of N n current and prevous update round, respectvely. P D INR s the number of update perods the IN node went through. P D INR = ndcates that the IN node s a new entrant n INR. If P D INR >, n s a stable IN node of N. st < and st > ndcate that the movement trace of n and N s n the opposte and same drecton n most update perods, respectvely. The larger the stablty value of IN nodes s, the hgher the probablty of IN nodes stayng n INR s. Followng these conclusons, the nformaton assocated wth each tem n NL s extended from a 2-tuple to a 3-tuple, as follows: n c ((X c, Y c ), sp c, st c ) where st c s the stablty value. D. Communcaton qualty forecast model As already mentoned, the path of cooperatve fetchng from suppler to N ncludes two parts: N to CN and CN to suppler. Before N fetches a new vdeo sequence from the suppler n x at t k+1, t needs to nvestgate the communcaton qualty n path INs nx from IN nodes (CN canddates) to n x and path N INs from N to IN nodes, n order to reduce the vdeo resource fetchng cost and download tme. (1) estmaton of communcaton qualty n path INs nx. N requres all members (ther stablty values are greater than ) n the IN node subset INS (n a, n b,..., n h ) to detect the avalable bandwdth to n x. The avalable bandwdth set AB (O) x (B(O) x (t 1), B (O) x (t 2),..., B (O) x (t k)) from each IN n- ode n to n x can be obtaned as descrbed n our prevous work [36], where t 1, t 2,..., t k denote the detecton perod tme. The orgnal seres AB (O) x needs to be preprocessed to an accumulated seres AB (A) x (B(A) x (t 1), B (A) x (t 2),..., B (A) x (t k)), accordng to eq. (19): B (A) x (t v) = { v B (O) x (t c) v = 1, 2,..., k} (19) By makng use of the accumulated seres AB (A) x the Grey Forecast Model GM(AB (A) x dfferental equaton of GM(AB (A) x db (1) to buld ) [34][35], the frst-order ) s defned as n eq. (2): + ab (1) = u (2) dt In eq. (2) t s the tme seres varable and B s the seres varable of bandwdth accumulaton wth ncreasng detecton tme. a and u denote the Grey level of development and control, respectvely. Eq. (21) descrbes the calculaton method for solvng the value of a and u accordng to the ordnary least square method. Û = [ â û ] = (D T D) 1 D T y (21) In eq. (21) â and û solutons for a and u, respectvely. D T s the transposton matrx of 1 2 [B(A) x (t 2) + B (A) x (t 1)]1 D, D = 1 2 [B(A) x (t 3) + B (A) x (t 2)]1... and 1 2 [B(A) x (t k) + B (A) x (t k 1)]1 y = (B (O) x (t 2), B (O) x (t 3),..., B (O) x (t k)) T. Makng use of â and û to solve eq. (2) we obtan eq. (22). ˆB(v + 1) = (B (O) x (t 1) û â )e âv + û (22) â ˆB(v + 1), v k and ˆB(v + 1), v > k denote the fttng and forecast values, respectvely. Let C x (v) = B (O) x (t v) ˆB x (v), v = 2, 3,..., k denote the resdual value. The resdual mean value and varance s obtaned accordng to eq. (23). C x = C x (c) (C x (c) C x ) 2, F x = k 1 k 1 c=2 The posteror varance rato R (P ) x are obtaned accordng to eq. (24). R (P ) x (23) and ts probablty P (P ) x = F x σ x, P (P ) x = P { C x(v) C x <.6745σ x } (24) where σ x s the varance of bandwdth n k update perods. σ x s defned as n eq. (25). (B (O) x (t c) σ x = k B (O) x )2 B (O) x (t c), B x = k (25) R (P ) (P ) x and P x can reflect the volatlty level of bandwdth n the path of data delvery durng the detecton perod tme, so they are used to estmate the lnk relablty n the path of data transmsson. We set two threshold values T H (R) and T H (P ) n the context of the Grey Forecast Model n order to measure the lnk relablty n the transmsson path. If R (P ) x T H(R) and P (P ) x T H (P ), the lnk state of path x s relable. N flters out all the members whch have unrelable lnks n INS and obtans a new IN subset INX. (2) The estmaton approach of communcaton qualty n path N INs s the same wth that of path INs nx. N detects the avalable bandwdth to each member n h n INS and obtans a bandwdth set AB (O) h and an accumulated seres AB (A) h accordng to eq. (19). In terms of soluton of the bult Grey Forecast Model GM(AB (A) h ), we can obtan the posteror varance rato R (P ) (P ) h and P h. We stll use T H(R) and T H (P ) to evaluate the relablty of lnk state of path h. N flters out the unrelable members n INS and also obtans a new IN subset INI.

8 8 The members of INX INI are consdered CN canddates. We need to calculate the bandwdth forecast expectaton (F ) (F ) (F ) value B x = mn( B, B x ) for each member n n INX INI at [t k+1, t v ], v [k + 1, 2k]. Ths s done accordng to eq. (22). E. Cooperatve Fetchng Algorthm In order to select the approprate CN, we make use of the Grey Relatonal Analyss (GRA) [37] to estmate the forecast bandwdth and stablty of these CN canddates. The stablty and forecast bandwdth are consdered as the estmated parameters and are normalzed accordng to eq. (26). x (att) = x (att) lower att upper att lower att, x (att) [, 1] (26) In eq. (26) att and x (att) denote estmated parameter (stablty and forecast bandwdth) of n and ts value, respectvely. lower att and upper att are mnmum and maxmum correspondng to the current estmated parameter att for all CN canddates, respectvely. The Grey Relatonal Coeffcent (GRC) of these canddates s obtaned accordng to eq. (27). GRC = 1 watt x (att) 1 + 1, GRC [, 1] (27) In eq. (27), w att s the weght value of x (att) and each parameter has a dfferent value of w att. If we focus on the stablty, the value of w att assocated to stablty should be greater than that of bandwdth. The canddate whch has the maxmum value of GRC s selected as N s CN. Consequently N sends a cooperatve fetchng request message to CN and makes preparatons for recevng streamng data from the CN. The request message between nodes has the followng format: req = (vid, spt, len, source), where vid dentfes the requested vdeo sequence, spt and len are the start pont and length of the requred vdeo chunk, respectvely and source s the source suppler whch has the vdeo content. As soon as the CN receves the vdeo data from n x, t forwards the receved data to N. The pseudo-code of the above process s detaled n Algorthm 1. F. Computaton Complexty We analyze the computaton complexty of CCF n the process of CN selecton. (1) CCF estmates the moblty stablty of the tems n S whch have experenced one or multple update perod(s) of N and are consdered as CN canddates. The computaton complexty for calculatng the volatlty and stablty of moblty of IN nodes s O(v) durng an update perod of IN nodes, where v s the length of S. The number of the tems n S determnes the cost for the calculaton of moblty stablty of IN nodes. Wth ncreasng update tmes of IN nodes, the computaton complexty s O(rv), where r denotes the number of tmes of updatng the IN nodes. (2) CCF selects the IN nodes whose stablty values are greater than from S to detect and estmate the communcaton qualty n the path of cooperatve fetchng. The Algorthm 1 Cooperatve fetchng algorthm for N 1: //count(s) s the sze of S set; 2: for( = ; < count(s); + +) 3: computes stablty st for each IN S[] by eq. (18); 4: f st > 5: put S[] nto INS 6: end f 7: end for 8: for( = ; < count(ins ); + +) 9: computes posteror varance rato R (P ) x, P (P ) x, R(P ), P (P ) for each IN node INS [] by eq. (24); 1: f R (P ) x T H(R) && P (P ) x T H(P ) 11: put INS [] nto INX ; 12: end f 13: f R (P ) T H (R) && P (P ) T H (P ) 14: put INS [] nto INI ; 15: end f 16: end for 17: for( = ; < count(cns = INX INI ); + +) 18: normalzes evaluaton attrbuton of CNS [] by eq. (26); 19: calculates GRC of CNS [] by eq. (27); 2: end for 21: selects tem n whch has maxmum value of GRC n CNS ; 22: send request message to n ; 23: N makes preparatons for recevng forwarded data from n ; 24: N receves multmeda data; selected IN nodes form a subset INS S. The computaton complexty of communcaton qualty s O(v). (3) The tems n INS whch have the relable lnk n the path of cooperatve fetchng form the CN canddate set CNS. CCF selects the CN from CNS n terms of ther predcted bandwdth and stablty. The computaton complexty of CN selecton algorthm s O(v) due to CNS S. The computaton complexty of CCF s O(rv). IV. PERFORMANCE EVALUATION A. Smulaton Settngs and Scenaros The transmsson effcency of vdeo data n the path of cooperatve fetchng determnes the performance of ths fetchng. CCF dvdes the path nto two parts: from the downloader to CN and from CN to the content suppler, and employs two models to address the problems n the delvery capacty of vdeo content caused by the moblty of moble nodes. (1) CCF nvestgates the moblty stablty of the IN nodes to ensure a stable one-hop neghbor relatonshp between the downloader and CN, whch obtans hgh transmsson capacty n the path from the downloader to CN. (2) CCF estmates the lnk relablty and forecasts the bandwdth n the path of cooperatve fetchng. The performance of the proposed CCF s compared aganst two classc algorthms: the Geographcal Dstance-based Neghbor selecton (GDN) and Avalable Bandwdth-based Neghbor selecton (ABN), whch are descrbed n [22]. GDN focuses on the geographcal dstance between the downloader and CN, so the CN node selected needs to have the closest geographcal dstance to the downloader. ABN nvestgates the communcaton qualty n the path from CN to content suppler, so the one-hop neghbor whch has the hghest bandwdth to the content suppler s consdered as a CN node. Therefore, GDN and ABN are smlar to CCF n the CN selecton strategy and are sutable

9 9 for the performance comparson wth CCF. Network Smulator verson 2 (NS-2) s used for modelng and smulatons. Next the setup for the common smulaton envronment for testng the three solutons s dscussed. Table II lsts some mportant NS2 smulaton parameters of the wreless network. In order to perform a closer-to-realty smulaton n a moble wreless network envronment, we defned sx smulaton scenaros n whch the moble speed of nodes s set to the followng sx ranges: [1-5], (5-1], (1-15], (15-2], (2-25] and (25-3] m/s. When a moble node reaches the specfed destnaton, t mmedately restarts ts movement wth a new assgned speed and destnaton. As ABN and GDN do not update the IN lst, we assume they use a statc tmer-based update scheme n order to compare the mantenance overhead of IN nodes wth ABN and GDN. Prevous research studed the effect of settng of the statc update perod [33]. Short perods (1-2 seconds) obtan fast real-tme nformaton about the IN nodes, but ncur hgh message overhead. On the contrary, long perods (7-1 seconds) reduce the message overhead, but cannot obtan the state of the one-hop neghbors n real-tme. In order to balance the mantenance cost and fast obtan the real-tme state of one-hop neghbors, the update perod tme of ABN and GDN s set to 4.5 s. The ntal update perod tme sv n CCF s set to 1 s. The two thresholds T (R) and T (P ) n terms of the evaluaton model n Grey Forecast Model [34][35] are set to.5 and.8, respectvely. The weght factors of stablty and bandwdth n eq. (27) are set to.5. ABN, GDN and CCF select 1 moble nodes as the requesters. The perod tme of detecton bandwdth s set to 1 s. After estmaton and forecast of communcaton qualty, these requesters select ther CN nodes and requre them to fetch the vdeo chunk from the server. The length of vdeo chunk s set to 3 s. When these TABLE II SIMULATION PARAMETER SETTINGS FOR THE WIRELESS NETWORK Parameters Values Area 8 8(m 2 ) Antenna Type Antenna/OmnAntenna Bandwdth of Each Moble Node 2 Mb/s Channel Channel/WrelessChannel Default Dstance between Server and Nodes 6 hops Data Transmsson Rate 2 Mb/s Basc Transmsson Rate 1 Mb/s Interface Queue Type CMUPrQueue Length of Vdeo Chunk 3 s MAC Interface MAC/82 11 Movement Drecton of Each Moble Node Random Network Interface Phy/WrelessPhy n sd MAX 2 m n sp MAX 1 m/s Number of Moble Nodes 4 Peak Moblty Speed 3 m/s Pause tme of Each Moble Node s Rate of Streamng Data 48 kb/s Routng Protocol DSR Smulaton Tme 41 s Transmsson Protocol of Vdeo Data UDP Transmsson Protocol of Control Messages TCP Wreless Sgnal Range 2 m requesters have receved 3 s streamng data, they re-select the CN node after the estmaton and forecast of communcaton qualty and receve the data from the CN nodes. The number of teratons s set to 1. The sx smulaton scenaros are repeatedly tested sx tmes, respectvely. B. Performance Evaluaton The performance of CCF s compared wth that of GDN and ABN n terms of average CN selecton accuracy, average endto-end delay, average packet loss rato (PLR), average throughput, mantenance overhead of the IN nodes and estmated user perceved qualty measured by Peak Sgnal-to-Nose Rato (PSNR) as defned by eq. (14) [38]. These metrcs are computed for dfferent moblty levels of the nodes. Increasng moblty s consdered n terms of the sx speed ntervals. Ths s as the moble nodes speed s a sgnfcant factor n the process of CN selecton. The mean value of smulaton results of sx repeated testng for each smulaton scenaro s used to llustrate the performance dfference between the three strateges compared. (1) Average CN selecton accuracy. The request nodes (requesters) select CN from ther IN nodes to obtan the requested vdeo chunk. The selected CNs connect wth the meda server and forward the vdeo data from the server to the requesters. The above process of cooperatve fetchng also s consdered as a CN selecton problem. If the selected CN mantans the IN relatonshp wth the requester n the process of cooperatve fetchng, the CN selecton s consdered accurate. The number of accurate selecton tmes dvded by the number of total CN selecton tmes s used to ndcate the average CN selecton accuracy. Fg. 4 shows the average CN selecton accuracy wth ncreasng node moblty. The blue curve shows how CCF CN selecton accuracy has hgh values between.8 and 1, despte a slght decrease wth the ncrease n node moblty. The red curve ndcates how GDN s results drop sharply wth ncreasng node moblty from ntal hgh average CN selecton accuracy to a value of.45, more than 7% lower than that of CCF. The green curve correspondng to ABN also decreases wth ncreasng node moblty, but ts correspondng accuracy values are always much lower than those of CCF and GDN. The fgure clearly shows how CCF outperforms GDN and ABN n terms of performance. By consderng the stablty value for the IN nodes, the requestng node and CN nodes have smlar speed and drecton and the IN relatonshp s mantaned over a relatve long-term perod. Consequently, the ncreasng node moblty varaton does not affect the performance of CCF. In GDN, the CN nodes stay close to the requestng node n the transtory perod, but rapdly lose the IN relatonshp wth the requester when the moblty ncreases. The selecton strategy of CN node n ABN reles on bandwdth to server estmaton, neglectng any locaton-related factors. Therefore, for ABN, the hgher the node moblty s, the lower the average CN selecton accuracy s. (2) Average end-to-end delay. The requesters select CN from ther IN nodes to obtan the requested vdeo chunk wth

10 Average selecton accuracy Agerage end-to-end delay(s) [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Fg. 4. Average CN selecton accuracy Fg. 5. Average end-to-end delay a length of 3 s. The selected CNs connect wth the meda server to obtan the vdeo chunk. The server transmts the vdeo chunk at the 48 kb/s data transmsson rate. The CNs use the same transmsson rate to forward the receved data to the requesters. The delay of vdeo data receved by CNs and requesters s used to ndcate the end-to-end delay, namely the mean value of average transmsson delay of two paths: from server to CNs and from CNs to requesters s consdered as average end-to-end delay n the wreless network. The average delay s defned as: where m =1 D (r) AD = and m =1 D (r) + n =1 D (c) (28) N(s) + N(c) n are the delay of the data =1 D (c) receved by requesters and CN nodes, respectvely. N(s) and N(c) are the data tems receved by requesters and CN nodes, respectvely. In Fg. 5, we llustrate how the average end-to-end delay vares wth the ncrease n the node moblty varance, when CCF, ABN and GDN are used n turn. It can be seen how the average end-to-end delays of ABN and GDN are mantaned at relatvely hgh levels and ncrease fast (the delay values are between.8 s and.25 s). CCF s average end-to-end delay curve keeps a low level wth slght ncrease durng the moble nodes ncrease n moblty. The delay values are between.2 s and.7 s, three tmes than those of ABN and GDN. The results shown by Fg. 5 also ndcate how wth ncreasng node moblty, CCF s performance benefts become even more evdent. The up to 3% better CCF results than those of ABN and GDN are due to the fact that CCF montors the IN nodes and estmates ther stablty. The stablty measurement for IN nodes ensures the CNs mantan the IN relatonshp wth the requester for longer. By estmatng the stablty of each IN, the nfluence of moble nodes moblty on CCF s performance s reduced, unlke ABN and GDN. Moreover, CCF makes use of the estmaton and forecast of communcaton qualty n the server-to-cn and CN-to-requester paths. The hgh avalable bandwdth and relable lnk state ensure low average end-toend delay. In GDN and ABN, the selected CN only depends on Average end-to-end delay (s) Fg. 6. Average end-to-end delay(s) Fg [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Average end-to-end delay n the CN-to-requester path [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Average end-to-end delay n the server-to-cn path the nstantaneous closest geographcal dstance to the requester and hghest bandwdth value from CN node to server, but the closest dstance and hghest bandwdth are dynamcally changng wth the movement of moble nodes. Wth ncreasng moble nodes speed, the rapdly ncreasng dstance between two nodes and hgh dynamc network topology ncreasngly affect the performance of vdeo data delvery. In order to clarfy further the average delay, we llustrate the average end-to-end delay n the CN-to-requester and serverto-cn paths n Fg. 6 and Fg.7. Average end-to-end delay n the CN-to-requester path: The average end-to-end delay n the CN-to-requester path s

11 11 defned as: AD c r = m =1 D (r) N(s) (29) where AD c r denotes the average delay n the CN-torequester path. Fg. 6 shows the average delay n CN-torequester path for CCF, ABN and GDN wth ncreasng node moblty varance. The curves correspondng to the results of ABN and GDN are at the hgh levels and have rapdly ncreasng trends (.e. GDN s curve ncreases from.3 s to.5 s and ABN s results have faster ncrease than GDN from.3 s to.7 s). The curve correspondng to the CCF results experences a slow ncrease and slght fluctuatons, wth values roughly 3% lower than those of ABN and GDN. Fg. 6 clearly shows how CCF outperforms ABN and GDN n terms of performance. CCF nvestgates the IN nodes speed and drecton of movement and geographcal dstance from the requester n order to estmate ther stablty. If CN nodes have stable IN relatonshp wth the requester, the data delvery over one-hop s performed wth low delay. Moreover, montorng and forecastng of communcaton qualty n the CN-to-requester path ensures hgh avalable bandwdth and relable lnk state. The relatvely good communcaton qualty reduces the average delay between the requester and CN node. GDN selects CN nodes n terms of close geographcal dstance to requester and neglects the communcaton qualty between CN nodes and requester. Increasng dstance between CN node and requester wth moblty varaton of nodes results n hgher average delay n the CN-to-requester path than for the case when CCF s employed. The selecton of CN nodes n ABN reles on the bandwdth between CN nodes and the server and t does not consder the key factors - dstance and bandwdth from the CN node to the requester. Consequently, n ncreasng node moblty condtons, ABN has the hghest average delay among CCF, ABN and GDN. Average end-to-end delay n the server-to-cn path: The average end-to-end delay n the server-to-cn path s defned as: AD s c = n =1 D (c) N(c) (3) where AD s c denotes the average delay n the server-to- CN path. Fg. 7 shows the average end-to-end delay n the server-to-cn path for CCF, ABN and GDN, respectvely. The average delays of ABN and GDN are mantaned at hgh levels and grow fast from.15 s to.4 s wth the ncrease n node moblty. It can be noted that the delay values and ncrease range of GDN are hgher than those of ABN. CCF s delays are kept low and experence slght growth from.4 s to.13 s only wth node moblty. As the CCF average delay values are roughly 2% lower than those of ABN and GDN, t can be concluded that CCF outperforms ABN and GDN n terms of delay-related performance. Average PLR Fg [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Average Packet Loss Rato Node moblty varaton (m/s) CCF not only nvestgates the lnk relablty of CN node n the server-to-cn path, but also forecasts the avalable bandwdth to the server. The relatvely good communcaton qualty between CN node and server enhances the effcency of data delvery and reduces the delay. The CN nodes have the hghest bandwdth to the server n ABN, but ABN does not evaluate the lnk relablty and does not forecast the avalable bandwdth, whch can change fast n a dynamc network envronment and negatvely nfluence the effcency of data transmsson. Unlke CCF and ABN, CN nodes n GDN do not have any a pror knowledge of bandwdth n the server-to-cn path, so GDN s delay values are the hghest among the three solutons studed. (3) Average packet loss rato (PLR). The total number of lost packets dvded by the total number of sent packets ndcates the PLR. The mean value of PLR of two paths: from server to CNs and from CNs to requesters s consdered as average PLR n the wreless network. Fg. 8 llustrates the varaton of the average PLR for CCF, GDN and ABN wth the ncrease n moble nodes moblty. In general, average PLR of GDN and ABN s mantaned relatvely hgh (between.4 and.13) and presents rapd growth wth ncreasng moble nodes moblty. At the same tme, CCF s average PLR has low values and exhbt slow ncrease, less than.5 durng the whole duraton of the tests. The results shown n Fg. 6 clearly ndcate how CCF outperforms both GDN and ABN n terms of PLR-based performance. As already known, hgh transmsson effcency and low PLR are drect effects from havng good communcaton qualty n the transmsson path. By makng use of montorng and forecastng of communcaton qualty n the server-to-cn and CN-to-requester paths, CCF selects CN nodes whch have both hgh avalable bandwdth and relable lnks to cooperatvely fetch vdeo content, so the average PLR s mantaned low. ABN only consders the bandwdth from the CN node to the server, so t cannot cope wth varaton of communcaton qualty n the server-to-cn and CN-to-requester paths wth ncreasng moblty of moble nodes. GDN does not consder the factors relatve to communcaton qualty so that t cannot ensure low PLR. On the other hand, the large number of ntermedate nodes n the transmsson path ncreases the probablty of packet loss. CCF, by estmatng the stablty

12 Average PLR Average PSNR(dB) [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Fg. 9. Average PLR n CN-to-requester path Fg. 12. Average PSNR Average PLR Fg. 1. Average throughput (kbps) Fg [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Average PLR n the server-to-cn path [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Average throughput of each IN node, mantans stable the IN relatonshp between CN and requester relatvely long-term wth ncreasng moble nodes moblty, so CCF s average PLR n the CN-to-requester path s kept very low. GDN reles on close dstance between the CN and requester whch changes fast wth ncreasng node moblty, so GDN s average PLR n the requester-to-cn path rses relatvely fast. ABN neglects the locaton factor relatve to the requester so the ncreasng dstance between the CN node and requester leads to PLR ncrease n the requester-to- CN path wth the ncrease n the moblty of moble nodes. Fg. 9 and Fg. 1 llustrate the average PLR n the CN-torequester and server-to-cn paths. Average PLR n the CN-to-requester path: Fg. 9 plots the average PLR between the requester and CN nodes for CCF, ABN and GDN wth ncreasng moblty of moble nodes. PLR values for ABN and GDN present rapdly ncreasng trends, from.4 to.8 for GDN and from.3 to.13 for ABN. CCF s values shows low ncreases, wth values roughly 2% lower than those of ABN and GDN. By nvestgatng the moblty-related factors of the IN nodes - speed, drecton and geographcal dstance from the requester, CCF estmates the stablty n terms of ther movement. The long-term IN relatonshp between the requester and CN nodes ensures low PLR, namely the one-hop delvery can reduce the packet loss probablty. Moreover, by makng use of the montorng and forecastng of communcaton qualty, the relable lnk state and hgh avalable bandwdth enhance the success rate of data delvery. Unlke CCF, GDN does not deal well wth the moblty of moble nodes. The CN whch has the closest geographcal dstance wth the requester may fast leave the INR of requester so that the mult-hop transmsson between CN and requester ncreases PLR. Moreover, GDN does not consder the communcaton qualty between the CN node and requester, so GDN s PLR presents fast ncrease. In ABN, the selecton of CN node depends on the bandwdth between the CN node and server so that the dstance and communcaton qualty relatve to the requester are neglected. In low node moblty stuatons, ABN has smlar PLR wth GDN, but ts PLR fast rses wth ncreasng moblty levels. Consequently, the performance of vdeo data delvery ncreasngly declnes due to the growng dstance between the requester and CN selected. Average PLR n the server-to-cn path: As Fg. 1 shows, the average PLR between the server and CN nodes of CCF, ABN and GDN mantans a rsng trend wth ncreasng moblty varaton of moble nodes. PLR of ABN and GDN fast ncrease from.4 to.13, and from.5 to.15, respectvely. PLR values and ncrease range of GDN are hgher than those of ABN. CCF s PLR values are mantaned at low levels, roughly 2% lower than those of ABN and GDN, and have a slow rse from.2 to.6. By montorng and forecastng the communcaton qualty from the IN nodes to server, CCF can dscover the CN nodes whch have relatvely good communcaton qualty n the

13 13 server-to-cn path. CCF s PLR s kept low wth ncreasng moblty of the moble nodes. In ABN, the CN node has the hghest bandwdth value n the server-to-cn path. The bandwdth value n the dynamc network envronment may have severe fluctuatons, so ABN s PLR values fast rse wth the moblty varaton of moble nodes. GDN s unaware of bandwdth and lnk state n the server-to-cn path, so GDN s PLR values vary the most n comparson wth CCF and ABN. (4 and 5) Average throughput and vdeo qualty. The average throughput s defned as: where m =1 SZ (r) m =1 SZ (r) T HR = (31) m h tme (r) s the amount of data receved by all requesters. m and h are the number of requesters and cooperatve fetchng, respectvely. tme (r) s the tme sum of recevng data. T HR denotes the average throughput obtaned by every requester n a cooperatve fetchng process. Fg. 11 compares the average throughput of CCF, ABN and GDN. The blue hstograms correspondng to CCF results outperform ABN and GDN (red and green hstograms), wth roughly 1%. ABN and GDN have smlar results n terms of performance dfference relatve to CCF wth ncreasng moblty varaton of moble nodes. Vdeo qualty, expressed n PSNR and measured n decbels (db), s estmated accordng to eq. (32) as vdeo qualty gets nfluenced by the delvery through the communcaton channel [38]. MAX Btrate P SNR = 2 log 1 ( ) (32) (EXP T hr CRT T hr) 2 In eq. (3) MAX Btrate s the average btrate of the multmeda stream as resulted from the encodng process, EXP T hr denotes the average throughput expected from the delvery of the multmeda stream over the network and CRT T hr ndcates the actual throughput measured durng delvery. M AX Btrate and EXP T hr are 48 kb/s n terms of smulaton settngs, respectvely. We make use of T HR to obtan PSNR of sngle vdeo streamng correspondng to every requester n a cooperatve fetchng process. Fg. 12 shows the average vdeo qualty correspondng to the average throughput of CCF, ABN and GDN wth ncreasng moblty of the moble nodes. The blue bars correspond to CCF s results and have ntal hgh average values, close to 3 db, excellent n terms of vdeo qualty. Despte hgh node moblty ncrease, the CCF vdeo qualty decreases down to a mnmum of 2 db, whch s stll consdered good for wreless transmssons more than 5% hgher than those of ABN and GDN. The red hstograms correspondng to ABN s results are smlar wth those of GDN and drop from good vdeo qualty levels of roughly 2 db n low node moblty stuatons to low qualty levels of 15 db wth ncreasng moblty varaton. CCF makes use of montorng and forecastng of stablty and communcaton qualty to obtan the hgh delvery performance of vdeo data. CCF s mantans average throughput at hgh levels, whch n conuncton wth low PLR, results n hgh qualty levels for vdeo streamng. However, ABN and GDN do not adapt well to the dynamc network envronment so that they have both lower throughput and hgher PLR and consequently, lower qualty levels for vdeo data delvery. (6) Mantenance overhead of the IN nodes. The mantenance overhead s expressed n terms of number of messages requred for the IN nodes selecton for every requester. The mantenance overhead reles on the number of IN nodes and update perod tme. The more IN nodes and less update perod can ncrease the mantenance cost. The purpose of comparson for GDN, ABN and CCF n mantenance overhead shows the performance dfference between statc and dynamc update perod. Fg. 13 (a), (b) and (c) show how the mantenance overhead of the IN nodes vares wththe ncreasng moblty of the moble nodes when the number of moble nodes ncreases from 4 to 5 and 6, respectvely. The value of every bar denotes the number of messages exchanged between the requester and the IN nodes n gven speed range of node moblty. The red and green hstograms correspond to the results of GDN and ABN, respectvely. GDN and ABN employ a statc tmer-based update scheme for mantanng IN nodes, so they have the same mantenance overhead. GDN and ABN have both hgher values and fast decrease trends wth the ncrease n the speed varaton of moble nodes n the dfferent number of moble nodes. Ther mantenance overhead are n [4, 15], [5, 18] and [6, 2] ranges correspondng to 4, 5 and 6 moble nodes, respectvely. The mantenance overhead of GDN and ABN rses wth ncreasng number of moble nodes. The CCF results, llustrated wth blue bars are n [15, 25], [16, 26], [18, 28] ranges wth ncreasng number of moble nodes, wth lower values and varatons than those of GDN and ABN. The mantenance overhead n CCF presents arsng trend wth the ncreasng moblty of moble nodes, too, but the ncrement s much lower. Ths determnes the results of GDN and ABN to be between 8% and 2% hgher than those of CCF, respectvely, CCF outperformng both GDN and ABN. The man reason for ths dfference between the three solutons s that the requester n CCF regulates the update perod tme n terms of the varaton level of the IN nodes: the moble nodes ncrease the update perod to reduce the update frequency when the number of stable IN nodes s hgh and decrease the update perod to ncrease the update frequency otherwse. CCF also has low mantenance overhead n dfferent node densty stuatons, so the varatons n number and moblty of moble nodes nfluences CCF slghtly only. The contnuous self-regulaton characterstc of CCF (whch perceves the varaton of IN nodes membershp stablty) makes sure the performance advantage s n favor of CCF. In GDN and ABN, the performance s lmted due to the statc update perod. As the movement speed varaton of nodes s low (e.g., node speeds are between [1-5] or (5-1]), the IN nodes mantan relatve stable INRs as moble nodes wth low speed need several update perods before they leave ther INRs. In ths stuaton the number of detecton

14 14 Mantenance overhead Mantenance overhead Mantenance overhead [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) [, 5] (5, 1] (1, 15] (15, 2] (2, 25] (25, 3] Node moblty varaton (m/s) Node moblty varaton (m/s) (a) Number of moble nodes s 4 (b) Number of moble nodes s 5 (c) Number of moble nodes s 6 Fg. 13. Mantenance overhead messages s mantaned hgh (between many nodes), whch s unnecessary. As the movement speed of nodes ncreases, the number of detecton messages decreases. Ths s as many nodes leave INR and few stable IN nodes are left wth whch data s exchanged untl new nodes are onng. These new nodes would have to wat untl the next statc update perod to exchange data, whch s sub-optmal. GDN and ABN have the lower adaptablty for dynamc network envronment than CCF. Consequently CCF outperforms GDN and ABN also n terms of mantenance overhead. V. CONCLUSION AND FUTURE WORK Ths paper proposes CCF, a novel Cooperatve Content Fetchng-based strategy to ncrease the qualty of vdeo delvery to moble users n wreless networks. By montorng the movement of the one-hop neghbors and employng an nnovatve forecast model for communcaton qualty whch measures the lnk relablty and predcts the bandwdth n the transmsson path, CCF estmates cooperatve neghbor (CN) characterstcs n terms of both stablty and communcaton qualty. CCF consders the CN node selected as extensons of the local buffer and proposes an effcent cooperatve fetchng algorthm to mprove vdeo qualty levels. CCF s performance was assessed n comparson wth that of two classc CN selecton strateges - GDN and ABN va smulatons. The results show how CCF outperforms both GDN and ABN n terms of CN selecton accuracy, average end-to-end delay, average packet loss rato, mantenance overhead, average throughput and vdeo qualty levels n ncreasng node moblty condtons. 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Bonaventure, Explorng Moble/WF Handover wth Multpath TCP, ACM SIGCOMM Workshop on Cellular Networks, Aug She Ja receved the M.S. degree from Kunmng Unversty of Scence and Technology, Kunmng, Chna n 29. He s currently workng toward the Ph.D. degree n the Insttute of Network Technology, Beng Unversty of Posts and Telecommuncatons, Beng, Chna. Hs research nterests nclude next generaton Internet technology, wreless communcatons and peer-to-peer networks. Changqao Xu receved the Ph.D. degree from the Insttute of Software, Chnese Academy of Scences (ISCAS) n January 29. He was an Assstant Research Fellow n ISCAS from 22 to 27, where he was a Research and Development Proect Manager n the area of communcaton networks. Durng 27-29, he worked as a Researcher wth the Software Research Insttute at Athlone Insttute of Technology, Athlone, Ireland. He oned Beng Unversty of Posts and Telecommuncatons (BUP- T), Beng, Chna, n December 29, and was an Assstant Professor from 29 to 211. Currently, he s an Assocate Professor wth the Insttute of Network Technology, and Vce-Drector of the Next Generaton Internet Technology Research Center at BUPT. He has publshed over 1 techncal papers n prestgous nternatonal ournals and conferences ncludng IEEE transactons on moble computng, IEEE transactons on vehcular technology, IEEE transactons on broadcastng, and Proceedngs of ACM Multmeda. Hs research nterests nclude wreless networkng, multmeda communcatons, and next generaton Internet technology. Janfeng Guan receved hs Ph.D. degrees n communcatons and nformaton system from the Beng Jaotong Unversty, Beng, Chna, n Jan. 21. He s a lecturer n the Insttute of Network Technology at Beng Unversty of Posts and Telecommuncatons (BUPT), Beng, Chna. Hs man research nterests focus around moble IP, moble multcast and next generaton Internet technology. Hongke Zhang receved hs Ph.D. degrees n electrcal and communcaton systems from the Unversty of Electronc Scence and Technology of Chna n From 1992 to 1994, he was a postdoctoral research assocate at Beng Jaotong Unversty (BJTU), and n July 1994, he became a professor there. He has publshed more than 15 research papers n the areas of communcatons, computer networks, and nformaton theory. He s the author of eght books wrtten n Chnese and the holder of more than 4 patents. He s the chef scentst of a Natonal Basc Research Program ( 973 program). He s Drector of the Next Generaton Internet Technology Research Center at Beng Unversty of Posts and Telecommuncatons (BUPT) and Drector of the Natonal Engneerng Laboratory for Next Generaton Internet Interconnecton Devces at BJTU. Gabrel-Mro Muntean receved the Ph.D. degree from Dubln Cty Unversty, Dubln, Ireland, for research n the area of qualty-orented adaptve multmeda streamng n 23. He s a Senor Lecturer wth the School of Electronc Engneerng at Dubln Cty Unversty (DCU), Dubln, Ireland. He s a Co-Drector of the DCU Performance Engneerng Laboratory, Drector of the Network Innovatons Centre, part of the Rnce Insttute Ireland and Consultant Professor wth Beng Unversty of Posts and Telecommuncatons, Chna. Hs research nterests nclude qualty-orented and performance-related ssues of adaptve multmeda delvery, performance of wred and wreless communcatons, energy-aware networkng, and personalzed e-learnng. He has publshed over 18 papers n prestgous nternatonal ournals and conferences, has authored three books and 15 book chapters and has edted sx other books. He s an Assocate Edtor of the IEEE transactons on broadcastng, Assocate Edtor of the IEEE communcatons surveys and tutorals, and revewer for other mportant nternatonal ournals, conferences, and fundng agences.