Multi-Source Video Multicast in Peer-to-Peer Networks

Transcription

1 ult-source Vdeo ultcast n Peer-to-Peer Networks Francsco de Asís López-Fuentes*, Eckehard Stenbach Technsche Unverstät ünchen Insttute of Communcaton Networks, eda Technology Group ünchen, Germany {fcoass, Eckehard.Stenbach}@tum.de Abstract We propose a novel framework for mult-source vdeo streamng n peer-to-peer (P2P) networks. ultple vdeos are dstrbuted to all requestng peers explotng full collaboraton between the sources, the requestng peers and helper peers. Each source dstrbutes ts own vdeo sequence whle addtonally forwardng vdeo data receved from other sources. A sngle peer s selected to redstrbute a partcular vdeo block to the peers whch would lke to receve the vdeos. Our goal s to maxmze the overall throughput or alternatvely the aggregate vdeo qualty of multple concurrent streamng sessons. We also consder the specal cases of same throughput or same vdeo qualty for all streams. We formulate the rate allocaton and redstrbuton as an optmzaton problem and evaluate our framework for three dfferent scenaros. In the frst scenaro, the rate allocaton s ontly decded for all partcpatng peers. In the second scenaro, the rate allocaton s also decded ontly, but addtonally ether same rate or same vdeo qualty streams are enforced. Our thrd scenaro assumes separate dstrbuton for every source. In ths case, the peers dvde ther upload capacty equally among the dfferent vdeo sequences. Our results show the superor performance of ont rate allocaton compared to ndependent allocaton and the effectveness of our framework.. Introducton Durng the last years, vdeo streamng over the Internet has ganed large popularty and peer-to-peer (P2P) networks have become an alternatve to tradtonal clentserver vdeo dstrbuton. A multcast soluton based on * Ths work has been supported by DAAD-CONACYT(éxco) grant P2P networks avods the bottleneck problem and the usage of dedcated replcaton servers when the maxmum number of streams n an ndvdual meda server s exceeded []. owever, due to the lmted capacty and unrelablty of peers, multple sources provdng the same vdeo or parts of t may be requred n P2P streamng [2]. ultple sources are also present n applcatons, where the system nherently has multple senders provdng dfferent vdeos as, e.g., n a vdeo conferencng scenaro wth more than one actve sender. In ths paper, we develop a novel framework for vdeo delvery from multple sources to multple recevers n P2P networks. In ths work, we consder as partcpatng peers the source peers, the requestng peers and addtonal helper peers. We assume that all requestng peers and all sources need to receve all vdeos. The helper peers are not nterested n recevng the vdeos and ust contrbute ther resources durng dstrbuton. Our approach s nspred by utualcast [3], whch s an effcent mechansm for oneto-many content dstrbuton that maxmzes the overall throughput by explotng the upload capacty of all partcpatng peers. We extend the utualcast scheme from one source to multple sources and nvestgate the optmal rate allocaton for mult-source streamng applcatons. In our scheme each source dstrbutes ts own vdeo sequence and addtonally forwards blocks of vdeo receved from other sources to the rest of the requestng peers. ow much the source can redstrbute depends on the avalable upload capacty. At the same tme, each requestng peer forwards the blocks drectly receved from a source to the rest of the peers. Agan, the amount of redstrbuted content depends on the upload capacty. We determne the optmal rate allocaton among multple streamng sessons and evaluate the effectveness of our proposed framework for dfferent scenaros. In the frst scenaro, the rate allocaton s ontly decded for all partcpatng peers. In the second scenaro, the rate allocaton s also decded ontly, but addtonally, ether same rate or same vdeo qualty streams are enforced. In the thrd scenaro, we assume separate rate allocaton for

2 every source. In ths case, the upload capactes of all partcpatng peers are dvded equally among the dfferent vdeo streams. For our analyss, we assume that the upload capacty of each peer s the only constrant, whch s an assumpton motvated by the fact that peers usually have larger download capacty than upload capacty (e.g. DSL lnes) on the Internet. Wthout loss of generalty, we assume n the followng two actve sources n the P2P network. The vdeos are dvded n segments X and Y whch are further dvded n blocks X, X 2,, X N, and Y, Y 2,,Y N, for delvery. We also assume that all partcpatng peers have heterogeneous upload capactes and may store dfferent portons of vdeo blocks. For our evaluaton we consder two obectves, namely throughput maxmzaton and aggregate vdeo qualty maxmzaton. Exhaustng the upload capacty of each peer leads to the best system performance, whch becomes reflected by maxmum throughput or best possble vdeo qualty. Our results obtaned from extensve smulatons show the superor performance of ont rate allocaton compared to ndependent allocaton and the effectveness of our proposed framework. In partcular, n ths paper we make the followng contrbutons:. The desgn of a new mult-source multcast framework wth full collaboraton among the partcpatng peers, targeted for vdeo streamng envronments that nherently have multple senders wth dfferent vdeos. 2. The formulaton of rate allocaton as an optmzaton problem wth obectve functons that ether maxmze the throughput or the vdeo qualty of the system by exhaustng the upload capacty of each peer. 3. Translaton of our optmzaton problem to a lnear program whch s solved usng common optmzaton technques. 4. The evaluaton of dfferent multcast scenaros wth multple sources: sources wth on rate allocaton for dfferent rates or dfferent vdeo qualty streams, sources wth ont rate allocaton for same rate or same vdeo qualty streams, and sources wth ndependent rate allocaton. We refer to sources wth ont rate allocaton when the sources perform a ont rate allocaton decson durng a streamng sesson. On the other hand, we refer to sources wth ndependent rate allocaton to the sources that work separately, for whch ther upload capactes are dvded equally among the dfferent vdeo streams. The remander of ths paper s organzed as follows. We dscuss related work n Secton 2. In Secton 3 we ntroduce our novel framework and descrbe the global optmzaton strateges. In Secton 4 we evaluate the performance of our approach n terms of throughput and vdeo qualty. Secton 5 concludes the paper. 2. elated work Durng the last years, several one-to-many applcaton layer schemes for content dstrbuton have been proposed [4], [5], [6], [7]. ult-source P2P multcast applcatons recently have been used for collaboratve envronments such as conferencng or mult-player games. The classcal tree soluton for multcast schemes shows lmtatons such as falure node fraglty or addtonal delay when t s used n a mult-source context []. An addtonal lmtaton for tree solutons s the lmted collaboraton among all peers. On the other hand, the emergng P2P overlays known as unstructured and structured overlay show lmtatons for mult-source multcast such as scalablty [2], large overhead [3] or complex protocols [4]. In [8] a new technque called Unstructured ult-source ultcast s presented. Ths soluton bulds and mantans multcast dstrbuton trees from many sources on top of an unstructured base overlay. The model deals wth some lmtatons n the unstructured P2P overlay such as scalablty and large overhead. The authors n [9] ntroduce a dstrbuted vdeo streamng framework, whch shows the benefts of vdeo streamng from multple servers to a sngle recever. In [0], a P2P meda streamng model s proposed that nvolves multple sendng peers n one streamng sesson, whch uses a tomography-based sender selecton protocol to optmze the qualty at the recevers. ecently, the authors n [2] and [5] have proposed new approaches for content dstrbuton from multple sources to a sngle recever. Whle n [2] the authors present and evaluate an algorthm for the optmal bt allocaton n combnaton wth scalable vdeo technques for dstrbuted streamng envronments, n [5] the authors explot the smlar source concept to sgnfcantly mprove the download tme of a fle from multple sources to one recever. All these solutons are only partally collaboratve because the collaboraton among the sources s not consdered. In [3], utualcast s proposed as an effcent multcast mechansm for content dstrbuton from one source to multple requestng peers. utualcast concentrates on mprovng the overall throughput, and does not consder ate-dstorton optmzaton of vdeo delvery as t s done n ths paper. We consder the problem of rate allocaton for multple vdeo streams n a collaboratve envronment and we focus on how to obtan an optmal soluton that maxmzes the overall throughput or the overall vdeo qualty. 3. Framework The prncple desgn of our framework s motvated by the many-to-many content dstrbuton problem durng vdeo streamng sessons on peer to peer networks. Our soluton bulds on top of utualcast, whch s an effcent

3 soluton for one-to-many content dstrbuton. Our model dffers from prevous mult-source multcast approaches n that t uses a fxed network topology where all the partcpatng peers are fully nterconnected, ncludng the sources. Also dfferent to prevous work, our soluton performs a ont rate allocaton decson consderng the upload capactes of all partcpatng peers. Our proposed framework s llustrated n Fgure for two source peers, two requestng peers and one helper peer. In ths example, the peers S and S 2 are the sources, whch contan the vdeo sequences X and Y to be dstrbuted among all partcpatng peer, whle peers, 2 are the requestng peers, and peer s a helper peer. The peer does not request the vdeos, nevertheless, t contrbutes ts upload capacty to help dstrbutng the vdeos to the other peers. ere, we can see that all the peers are n fact recevers and senders at the same tme, as t s for nstance the case n a multpont vdeo conferencng scenaro. Each requestng peer forwards a receved vdeo block to the other requestng peers and the other source peers. Each source splts the orgnal content nto small blocks and one unque peer s selected to dstrbute a block to the rest of the peers. In our example, the source S dvdes the vdeo X nto the blocks X to X 5, whle the source S 2 dvdes the vdeo Y nto the blocks Y to Y 5. ecause our approach s based on collaboraton among sources, each source dstrbutes ts own vdeo whle addtonally forwardng the block of vdeo receved from the other source to the rest of the requestng peers. At the same tme, each requestng peer forwards the blocks drectly receved from a source to the rest of the partcpatng peers. Thus, the blocks (X, Y ), and (X 2, Y 2 ) are assgned to the requestng peers and 2, respectvely, whle the blocks (X 3, Y 3 ) are assgned to the helper peer, the block X 4 s assgned to the source peer S 2 and block Y 4 s assgned to the source peer S for X, X 2, X 3, X 4, X 5 Y X, X 5, Y 4 S Y 2 X X2, X 5, Y 4 X 2, Y 2 Y,Y 5, X 4 Y 4, Y 5 X 4, X 5 Y 2, Y 5, X 4 X 3 X, Y 2 X 3, Y 3 X 3, Y 3 Y 3 X 2 S 2 Y,Y 2,Y 3,Y 4, Y 5 Y3 X 3 Fgure. Our proposed framework for mult-source multcast. There are two sources, two requestng peers and one helper peer. dstrbuton. Peers wth dfferent upload capacty dstrbute a dfferent amount of content. The block sze assgned to each requestng peer s proportonal to ts upload capacty. When the source peers have abundant upload resources, each source addtonally sends one block drectly to the vdeo recevng peers. To llustrate such a case, Fgure shows that source S drectly sends block X 5 to each peer and source S 2 drectly sends block Y 5 to each peer. Thus, source S sends one block to each partcpatng peer for redstrbuton, one block n parallel to all requestng peers, and forwards one block of the vdeo Y receved from the source S 2 to each requestng peer. The source S 2 behaves smlar as source S, but n a complementary way. It sends the vdeo Y and forwards the vdeo block X 4. Each requestng peer forwards the blocks receved from the sources S and S 2 to the other requestng peers and the other sources, e.g., peer receves the blocks X and X 5 from source S and the block Y and Y 5 from the source S 2. After ths peer forwards the block X and Y to the rest of the partcpatng peers except to the source where the block was orgnally generated and the helper peer. The blocks X 3 and Y 3 are sent by the sources S and S 2, respectvely to the helper peer, whch forwards the blocks to all partcpatng peers except to the source where the block was orgnally generated. The two sources may not have the same upload capacty or there mght exst a specfc QoS requrement for dfferent content durng a vdeo streamng applcaton. Therefore, we must use a specal strategy at each partcpatng peer to optmze the bandwdth allocaton for the dfferent receved vdeo sequences. 3. Throughput and PSN-based optmzaton Our framework s based on utualcast [3], whch s formed by a source S of upload capacty S, N requestng peers wth an average upload capacty C, and N 2 helper peers wth an average upload capacty C. utualcast uses three dstrbuton routes. These are () through the content-requestng peers; (2) through the helper peers and (3) drectly from the source. The route 3 s chosen only when the source stll has upload capacty after exhaustng routes and 2. Thus, the utualcast dstrbuton throughput Θ, whch represents the amount of content sent to the requestng peers per second s defned [3] as S, S ( ), Θ = () S ( ) ( ) +, S > ( ), N where N = C and N 2 = C (2) N N

4 To extend the utualcast concept to our proposed collaboratve mult-source scheme, we consder sources S, S,..., S wth upload capacty 2 S, S,..., 2 S, respectvely, N content requestng peers and N 2 helper peers. The mean upload capacty of the requestng peers and the helper peers s agan denoted as C and C, respectvely. In the frst scenaro, the rate allocaton s ontly decded for all partcpatng peers. All partcpatng peers are fully connected and all of them, except the helper peers, need to receve all vdeos. Therefore, the maxmum dstrbuton throughput Θ n our mult-source scheme s gven by S, S ( + ), = = Θ = (3) ( + ) S > ( + ), S = = ( ) +, N + where N = C and N C N + 2 = 2 (4) N + An alternatve to throughput maxmzaton s to maxmze the overall end-to-end vdeo qualty for all delvered vdeos. In ths paper, the peak sgnal-to-nose rato (PSN) s used as a measure of vdeo qualty. In [6], two models have been developed for the sequence level dstorton-rate (D-) performance of predctve vdeo source encodng. oth models requre very lmted amount of emprcal data, namely three pars of rate and dstorton, n order to set up the model parameters. Expermental valdatons usng.264/avc encoded vdeo test sequences report hgh accuracy of these two proposed models. The D- model from [6] that we adopt n ths paper relates the PSN of a vdeo sequence to the encodng rate as c PSN ( ) = a + b (5) c where the parameter tuple (a, b, c) can be obtaned by measurng three pars of rate and dstorton. We use the.264/avc J reference software verson 2 [7] and select three dfferent quantzaton parameters to obtan three pars of PSN and rate for a specfc vdeo sequence. The overall end-to-end vdeo qualty of all delvered vdeos n our framework s gven by PSN total = = ( PSN ) (6) wth beng the number of vdeos (sources) n our P2P network. Dfferent sources may have dfferent upload capactes durng a vdeo streamng sesson and the vdeos may be streamed at dfferent rates. Therefore, a requestng peer may receve at the same tme dfferent vdeos wth dfferent qualty. In order to deal wth ths ssue, n our second scenaro we also ontly decde the rate allocaton for all partcpatng peers, but addtonally enforce ether that same rate or same vdeo qualty streams. For same rate streams, we enforce the dstrbuton throughput for all sources to be the same,.e., Θ = Θ for, k =. The k amount of data that source S uploads to the content requestng peers and the helper peers for redstrbuton s N N2 = + = (7) = k k Assumng that = and = for, k =, the dstrbuton throughput becomes Θ,..., = mn(,..., ) for mn( S S S,..., S ) ( + ) / (8) where and are gven by equaton (4). If mn( S,..., S ) > ( )/, then the sources S to S are not exhausted and ther resdual upload capactes are gven by S = S ( )/ for =. The exhauston of S to S s gven by ( N + ) +, , + ( N + 2)( 2, , ) S (9) ( N + ) +, , + ( N + 2)(, , ) S wth +, , = , = Θ =... = Θ =.. = Θ and Θ = = Θ. represents the vdeo block drectly sent from the source S to all N requestng peers and the other - sources.,2 represents the vdeo block sent by the source S to the source S 2, whch forwards the block to N requestng peers and -2 sources. represents the vdeo block drectly sent from the source S to N requestng peers and - sources, and, represents the vdeo block sent by the source S to the source S, whch forwards the block to N requestng peers and -2 sources. Θ s the addtonal throughput. Together wth Θ n (9), the overall throughput s then gven as,..., Θ = ( Θ,..., ) + Θ (0) The rate allocaton can be formulated as an optmzaton problem, whch can be solved usng lnear programmng [9]. When the same rate for both vdeo sequences s not enough to obtan a smlar vdeo qualty, we need to enforce that both vdeo sequences have the same PSN. The PSN enforcement s possble, when the sources have abundant upload capacty. To ths end, we manpulate the broadcast lnks n each source and ncrease the rate of the sequence wth the lowest PSN. We stop teratng when all sequences have smlar PSN or untl the upload capacty of the lowest rate source s exhausted. Our thrd scenaro consders the case where the sources work separately wth ndependent rate allocaton. We assume that all peers dstrbute ther upload capactes n an equal way among all dfferent vdeo sequences. In other words, separate utualcast dstrbutons run n

5 parallel and the ndvdual maxmum throughput for each vdeo can be wrtten as S /, S / () Θ = ( S / ) +, S / > N + N = / + / 2 = / wth = N C S C = +. Smlar N + 2 N + to the second scenaro we enforce the same rate for all vdeos. In ths case, the overall throughput becomes Θ = mn( Θ,..., Θ,..., Θ ) (2) 4. Evaluaton * We have evaluated our proposed framework n terms of overall throughput and aggregate vdeo qualty. To evaluate the vdeo qualty, we use the peak sgnal-to-nose rato (PSN), whch s the most wdely used obectve vdeo qualty metrc. A vdeo sequence wth a PSN value between 30 and 40 d usually s acceptable, whle a vdeo wth a PSN below 30 d s typcally qute bad [8]. We maxmze the overall throughput and the PSN usng lnear programmng for three dfferent cases:. Sources wth ont rate allocaton for dfferent rate streams or dfferent vdeo qualty streams, 2. Sources wth ont rate allocaton for same rate streams or same vdeo qualty streams, 3. Sources wth ndependent rate allocaton. For all cases, we use a set of 5 partcpatng peers, where two peers S and S 2 act as sources and three peers, 2 and 3 work as requestng peers. The upload capacty of the partcpatng peers S, S2,, 2 and 3 n kbps s 300, 600, 500, 300, and 300, respectvely. elper peers are not consdered n our evaluaton. 4. Throughput-based optmzaton 4.. Sources wth ont rate allocaton for dfferent rate streams In ths scenaro, the rate allocaton s ontly decded for all partcpatng sources and requestng peers. In order to maxmze the overall throughput, we consder that vdeo sequences X and Y can have a dfferent rate. We translate our assumptons nto a lnear program whch s shown n Fgure 2. ere, we can see that each source has to contrbute ts upload capacty not only to dstrbute ts orgnal content, but also the blocks receved from the other source. The frst constrant X +X 2 +X 3 +X 4 +4X 5 +3Y consders the upload capacty of source S. S has to delver the blocks X, X 2 and X 3 to the requestng peers, axmze Θ=X + X 2 + X 3 + X 4 + X 5 + Y + Y 2 + Y 3 + Y 4 + Y 5 subect to X + X 2 + X 3 + X 4 + 4X 5 + 3Y 4 300, Y + Y 2 + Y 3 + Y 4 + 4Y 5 + 3X 4 600, 3X + 3Y 500, 3X 2 + 3Y 2 300, 3X 3 + 3Y 3 300, 0 X, 0 Y, 0 X 2, 0 Y 2, 0 X 3, 0 Y 3, 0 X 4, 0 Y 4, 0 X 5, 0 Y 5, Fgure 2. Lnear program for case one. The obectve s to maxmze the overall throughput 2, 3, respectvely. Addtonally, source S exhausts ts upload capacty by sendng a block X 4 to source S 2 for redstrbuton to all requestng peers, by delverng a block X 5 drectly to each requestng peer and source S 2 and by redstrbutng block Y 4 receved from source S 2 to each requestng peer. Each requestng peer receves one block of X and Y from the sources S and S 2, and the upload capacty of each requestng peer s exhausted by redstrbutng the blocks to the rest of the requestng peers and one source. Smlar to the frst constrant n Fgure 2, the second constrant represents how the upload capacty of source S 2 s exhausted. The thrd, fourth and ffth constrants represent the contrbuton of the requestng peers, 2 and 3, respectvely. The constrants 0 X to 0 Y 5, mean that negatve block szes are not allowed. ere, f the block sze s zero, t means that no block s transmtted on ths lnk. To solve our lnear program, the athematca 5.0 software packet was used. The soluton gves a maxmum throughput of 500 kbps, whle the rate of the blocks n kbps s X = 83.33, X 2 = 50, X 3 = 50, X 4 = 6.67, X 5 = 0, Y = 83.33, Y 2 = 50, Y 3 = 50, Y 4 = 0, and Y 5 = 6.67, respectvely. The rate of the sequences X and Y are 300 kbps and 200 kbps, respectvely. In ths case, the best overall throughput can be acheved and the upload capacty of both sources and all requestng peers s fully exhausted. owever, the vdeo sequences X and Y have dfferent rate and hence most lkely dfferent qualty Sources wth ont rate allocaton for same rate streams In our second scenaro, the rate allocaton s also decded ontly for all partcpatng peers, but addtonally, we enforce the same rate for vdeo sequences X and Y. The resultng lnear program s smlar to the prevous case, except that now we assume that X +X 2 +X 3 +X 4 +X 5 = Y +Y 2 +Y 3 +Y 4 +Y 5. The soluton gves agan a maxmum throughput of 500 kbps, whle the rate of the blocks n kbps s X = 83.33, X 2 = 50, X 3 = 50, X 4 = 66.66, X 5 = 0, Y = 83.33, Y 2 = 50, Y 3 = 50, Y 4 = 6.66 and Y 5 = 50, respectvely. Now, the rate of the two sequences X and Y s 250 kbps and hence balanced. In ths case, the best overall throughput can be acheved, the upload capacty

6 of both sources and all requestng peers s fully exhausted, and the sequences X and Y have the same rate Sources wth ndependent rate allocaton Our thrd scenaro assumes ndependent rate allocaton for every source. For ths case, the sources dvde ther upload capacty equally among the two vdeo streams X and Y. Thus, for the delvery of each sequence, the upload capacty n each source s dvded by two. Each requestng peer also dvdes ts upload capacty equally between both sequences. The soluton after solvng the resultng lnear program gves a maxmum throughput of 300 kbps, whle the rate of the blocks n kbps s X = 83.33, X 2 = 50, X 3 = 6.66, X 4 = 0, X 5 = 0, Y = 83.33, Y 2 = 50, Y 3 = 6.66, Y 4 = 0 and Y 5 = 0, respectvely. The sze of the sequences X and Y are balanced wth 50 kbps, but the maxmum throughput s smaller than for the two prevous cases. In order to extend our experment, we now vary the upload capacty of the source S from 200 to 900 kbps n all cases, whle the upload capacty of the source S 2 and the requestng peers s mantaned. The results for the three cases are compared n Fgure 3. The results show that the mult-source multcast scheme wth same rate streams acheves the same maxmum overall throughput as the mult-source scheme wth dfferent rate streams, when an optmal bandwdth allocaton s found. Contrary, the mult-source scheme wth ndependent rate allocaton acheves an overall throughput smaller than for the multsource scheme wth dfferent rate streams and the multsource scheme wth same rate streams, because the throughput s lmted by the upload capacty of the weakest source. One can also see that all approaches lead to dentcal throughput when both sources have the same upload capacty Vdeo qualty-based optmzaton To evaluate our framework n terms of vdeo qualty, we frst ft the model n (5) for each vdeo sequence. ere, we use the test sequences Foreman and other and Daughter. The three D- par used to ft the model n Overall Throughpt (kbps) Scenaro_ Scenaro_2 Scenaro_3 source S 2 = 600 kbps Capacty of source S (kbps) Fgure 3. Overall throughput comparson for the three cases nvestgated Table. PSN and rate for two vdeo sequences Sequence PSN /ate (d/kbps) PSN 2 /ate 2 (d/kbps) PSN 3 /ate 3 (d/kbps) Foreman 28/40 4/500 45/000 other and Daughter 32/40 45/500 48/000 (5) are shown n Table. The resultng D- models for the two test sequences are as follows: Foreman: (a = 36.44, b= 4.75 and c= 97.97) = PSN ( ) other & Daughter: (a = 44.34, b= 4.05 and c= 425.8) = PSN 2( 2 ) Smlar to our throughput-based optmzaton, we evaluate the three dfferent scenaros ntroduced n the begnnng of Secton 4 and use a set of 5 partcpatng peers, where the two peers S and S 2 act as sources whle the three peers, 2 and 3 are requestng peers. To maxmze the sum of PSN of both vdeo sequences, we assume the same upload capacty settngs for each partcpatng peer as n the prevous secton. The system performance based on overall vdeo qualty s defned by equaton 6, where s set to Sources wth ont rate allocaton for dfferent vdeo qualty streams In the frst scenaro, we maxmze the sum of PSN for both sequences, wthout enforcng the same qualty for both sequences. We translate our assumptons nto the lnear program n Fgure 4. The set of constrants defned n our lnear program are the same as n Fgure 2. We solve our lnear program usng athematca 5.0. The soluton maxmzes the sum PSN = *Sqrt[( /97.97)]*( / ); PSN 2 = *Sqrt[( 2 /425.8)]*( / 2 ); = X + X 2 + X 3 + X 4 + X 5 ; 2 = Y + Y 2 + Y 3 + Y 4 + Y 5 ; axmze: PSN + PSN 2 subect to: X + X 2 + X 3 + X 4 + 4X 5 + 3Y 4 300, Y + Y 2 + Y 3 + Y 4 + 4Y 5 + 3X 4 600, 3X + 3Y 500, 3X 2 + 3Y 2 300, 3X 3 + 3Y 3 300, 0 X, 0 Y, 0 X 2, 0 Y 2, 0 X 3, 0 Y 3, 0 X 4, 0 Y 4, 0 X 5, 0 Y 5 Fgure 4. Lnear program for case one. The obectve s to maxmze the end-to-end vdeo qualty

7 of PSN to be d, whle the rates of the blocks n kbps are X = 83.33, X 2 = 50, X 3 = 50, X 4 = 75.53, X 5 = 6.68, Y =83.33, Y 2 = 50, Y 3 = 50, Y 4 = 4.79 and Y 5 = 46.3, respectvely. The rate of the sequences X and Y are kbps and kbps, respectvely. The PSN values of the frst and second sequence n d are and 4.90, respectvely. The upload capacty of all peers s exhausted and the vdeo qualty of the two streams dffers sgnfcantly Sources wth ont rate allocaton for same vdeo qualty streams In the second scenaro, we desre that the sequences generated n each source have the same PSN. Intally, we assume a rate of 250 kbps for both sequences n our lnear program, the soluton leads to a sum of PSN of 79.7 d. The PSN values of the frst and second sequence n d are and 42.6, respectvely. The resultng overall recepton qualty s close to the optmal qualty PSN obtaned by the mult-source scheme wth dfferent vdeo qualty, whch represents the best overall PSN n our framework, however the ndvdual vdeo qualty for both vdeos s dfferent. Then, we need to enforce the same PSN for both sequences. To ths end, source S 2 doesn t send block Y 4 to source S, to avod exhaustng the upload capacty of S durng the dstrbuton of ths block. Also, block Y 5 s not sent by the source S 2 n order to use ths upload capacty to send the block X 4 to all requestng peers. After ths, the PSN values of the frst and second sequence n d are and 4.2, respectvely. The sum of PSN s d. Ths result shows that the overall qualty s reduced, but both sequences tend to reach more smlar PSN. The frst sequence cannot reach the same PSN as the second sequence, because the upload capacty of S has been exhausted and the rate cannot be ncreased. If we want to have exactly the same vdeo qualty we would now have to reduce the rate of the second sequence whch however would lead to a waste of avalable resources as we would no exhaust all upload capactes Sources wth ndependent rate allocaton Fnally, we evaluate the overall vdeo qualty when the sources have ndependent rate allocaton and they dstrbute ther upload capactes equally to X and Y. We also assume that each requestng peer dvdes ts upload capacty equally to both vdeos. When solvng the correspondng lnear program, we obtan a sum of PSN of d. The rate of the blocks n kbps s X = 2.53, X 2 = 34.46, X 3 = 8.98, X 4 = 85.0, X 5 = 0, Y = 5936, Y 2 = 44.73, Y 3 = 4.75, Y 4 = 3.07 and Y 5 = 0.068, respectvely. The rate of the sequences X and Y s balanced wth 50 kbps and the frst and second sequences have a PSN of 35.2 d and d, respectvely, but the sum of PSN s smaller when compared to the prevous cases. To extend our experments, we now vary the upload capacty of the source S from 200 to 900 kbps, whle the upload capacty of the source S 2 and the requestng peers s mantaned. Fgure 5 compares the overall vdeo qualty for the evaluated scenaros. ere, we show the overall PSN for the mult-source scheme wth same vdeo qualty after the PSN enforcement has been made. We can see that when the source S s weak (e.g., 200 kbps), the PSN enforcement s not possble, because the maxmum rate reached by sequence X s dentcal to the upload capacty of S. The results show that the multsource scheme wth same vdeo qualty acheves an overall vdeo qualty close to the mult-source scheme wth dfferent vdeo qualty, but not equal. On the other hand, the mult-source scheme wth ndependent rate allocaton shows a smaller sum of PSN, because the vdeo qualty s lmted by the upload capacty of the weaker source. owever, all approaches lead to smlar overall PSN when both sources have the same upload capacty. In Fgure 6, we compare the ndvdual PSN of each vdeo sequence for each scenaro. We can observe that the ndvdual PSNs obtaned from the mult-source scheme wth same vdeo qualty present the best vdeo PSN (d) PSNtotal (Scenaro_) PSNtotal (Scenaro_2) PSNtotal (Scenaro_3) Capacty of the source S (kbps) Fgure 5. Overall PSN comparson for all cases PSN (d) PSN (Scenaro_) PSN2 (Scenaro_) PSN (Scenaro_2) PSN2 (Scenaro_2) PSN (Scenaro_3) PSN2 (Scenaro_3) Capacty of the source S (kbps) Fgure 6. Indvdual PSN comparson for all scenaros

8 qualty balance for both vdeo sequences. In contrast, the ndvdual PSNs obtaned for the mult-source scheme wth ndependent rate allocaton are less balanced. For ths specfc example, the equal PSN enforcement s requred because the vdeo sequences are very dfferent. owever, when the vdeo sequences from dfferent sources are smlar, a strategy based on same rate may be suffcent to obtan a smlar vdeo qualty for all vdeo sequences n all partcpatng peers. 5. Concluson In ths paper, we have proposed and evaluated a novel framework for vdeo streamng from multple sources to multple recevers n Peer-to-Peer (P2P) networks. We have formulated the rate allocaton problem n our framework as an optmzaton problem wth an obectve functon that ether maxmzes the throughput or the aggregate vdeo qualty of the system. We have solved our optmzaton problem usng lnear programmng for three dfferent scenaros. The results show that our framework for multple-source multcast wth same rate streams acheves an optmal performance n terms of overall throughput. Furthermore, the PSN enforcement helps us to balance the vdeo qualty of all vdeo streams receved by the partcpatng peers. Its performance s smlar to the maxmum possble performance, and better than the performance acheved by the mult-source multcast wth ndependent rate allocaton. Our framework s suted for collaboratve streamng envronments, where the system nherently has multple senders and vdeo sequences and smlar vdeo qualty s desred. Our current effort s focused to complete the mplementaton of our proposed framework on the PlanetLab nfrastructure n order to nvestgate ts performance durng real streamng sessons. 6. eferences [] E. Setton, J. Noh and. Grod; ate-dstorton Optmzed Vdeo Peer-to-Peer ultcast Streamng, n Proc. of the Workshop on Advances n Peer-to-Peer ultmeda Streamng at AC ultmeda '05, Sngapore, Nov [2]. efeeda, A. abb, D. Xu,. hagava and. otev; Collectcast: A Peer-to-Peer servce for eda Streamng, n AC/Sprnger ultmeda System Journal, vol., number 5, Nov. 2005, pp [3] J. L, P. A. Chou, C. Zhang; utualcast: An Effcent echansm for One-To-any Content Dstrbuton, n Proc. of the AC SIGCO ASIA Workshop, Aprl [4]. Castro, P. Druschel, A.. Kermarrec, A. Nand, A. owstron and A. Sngh; "SpltStream: gh-andwdth ultcast n Cooperatve Envronments," n Proc. of the 9 th AC SOSP, olton Landng, NY, USA, Oct [5] J. Jannott, D. K. Gfford, K.L. Johnson,. F. Kaashoek and J. W. O'Toole Jr.; Overcast: relable multcastng wth an overlay network, n Proc. of the 4th OSDI Symposum San Dego, CA, USA, Oct. 2000, pp [6]. aneree,. hattacharee and C. Kommareddy; Scalable Applcaton Layer ultcast, n Proc. of the 2002 AC SIGCO Conference, Pttsburgh, PA, USA, August 2002, pp [7] V.N. Padmanabhan,. Wang, P. Chou, K. Srpandkulcha; Dstrbutng Streamng eda Content Usng Cooperatve Networkng, n Proc. of the 2 th Internatonal Workshop NOSSDAV 2002, am, FL, USA, ay 2002, pp [8]. peanu, I. Foster, A. Iamntch, A. ogers; In Search for Smplcty: A Self-Organzng ult-source ultcast Overlay, n Proc. of the st IEEE SASO '03 Internatonal Conference, oston, A, USA, July [9] T. Nguyen, A. Zakhor; Dstrbuted vdeo streamng over the Internet, n Proc. of the 3 th Internatonal Workshop NOSSDAV 2003, onterey, CA, June [0]. efeeda, A. abb,. otev, D. Xu,. hargava; POISE: Peer-to-Peer eda Streamng Usng CollectCast, n Proc. of the AC ultmeda '03, erkeley, CA, USA, Nov [] D. Kostc, A. odrguez, J. Albrecht, A. Vahdat, ullet: gh andwdth Data Dssemnaton Usng an Overlay esh, n Proc. of the 9 th AC SOSP '03, olton Landng, NY, USA, Oct [2] Y. Chawathe, Scattercast: An Adaptable roadcast Dstrbuton Framework, n AC ultmeda Systems Journal Specal Issue on ultmeda Dstrbuton, [3]. peanu, I. Foster, and A. Iamntch, appng the Gnutella Network: Propertes of Large-Scale Peer-to-Peer Systems and Implcatons for System Desgn, n Internet Computng Journal, vol. 6, [4] A.. harambe, S. G. ao, V. N. Padmanabhan, S. Seshan, and. Zhang, The Impact of eterogeneous andwdth Constrants on DT ased ultcast Protocols, n Proc. of the 4th Internatonal Workshop IPTPS '05, Feb [5]. Pucha, D.G. Andersen,. Kamnsky; Explotng Smlarty for ult-source Downloads Usng Fle andprnts, n Proc. of the 4 th USENIX NSDI '07, Cambrdge, A, USA Aprl [6] L. Cho,. Ivrlac, E. Stenbach, J. Nossek, Sequencelevel models for dstorton-rate behavour, n Proc. of the IEEE ICIP 2005, Genova, Italy, Sep [7].264/PEG-4 AVC eference Software [8] Y. Wang, J. Ostermann, Y.Q. Zhang; Vdeo Processng and Communcatons, Prentce all 200. [9]. urger, T. Kelmann,. E. al, alanced ultcastng: gh-throughput Communcaton for Grd Applcatons, n Proc. of AC/IEEE Supercomputng, Seattle, WA, USA, Nov. 2005, pp