# Enabling P2P One-view Multi-party Video Conferencing

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5 5 peers to upload n each sub-conference. For general cases wth more sources, we have the followng result. Lemma : For any one-vew MPVC scenaro, we have G (I) = ( G ) (2) S S Proof: Snce a peer s ether an dle peer or a busy peer, thus = S + I. In addton, every peer watches exactly one vdeo. Hence = S G. So we have I = S G S = S ( G ) In addton, snce {G, S} s a partton of N, and G (I) = G I, then {G (I), S} s a partton of I N. Then we have S G(I) = I = S ( G ) Based on Lemma, we present an dle peer assgnment procedure that can guarantee each sub-conference wth G users can be assgned wth G dle peers n followng procedure. ) For a sub-conference where all vewers are dle,.e. G (I) = G, t wll only use G of ts own vewers to relay vdeo and B (H) =, t also contrbutes one dle peer to the common helper pool H; 2) For a sub-conference where exactly one vewer s a source,.e. G (I) = G, t wll only use all of ts own vewers to relay vdeo and B (H) =, t does not contrbute any peer to the helper pool H; 3) For a sub-conference wth G I < G, t wll use t own G I dle peers and G G I dle peers from the helper pool H to relay ts vdeo,.e., B (H) = G G I. If we use S, S 2 and S 3 to represent the set of subconferences n case ), 2), and 3) respectvely, then the number of helpers contrbuted to H by sub-conferences n S s: H = S G (I) ( G ) = k=2,3 S k G G I = S 3 G G I = S 3 B (H), where the second equalty s due to Lemma and the thrd equalty s due to G (I) = G for any sub-conference n S 2. Ths guarantees that the prevous helper allocaton scheme s feasble. Idle peers assgned to sub-conference of source nclude ts own dle vewers n G (I), and dle peers from other sub-conferences. Theorem 2: If all peers bandwdth s one, for any gven scenaro {G, S}, the achevable vdeo rate r for any sub-conference G satsfy: r = B(H) G 2 G + G Proof: In the prevous dle peer assgnment, (3) u (s) =, B (W ) + B (H) = G, S. (4) Accordng to Equaton, the achevable rate s Snce B (H) r = + G G G 2 = B(H) G 2. B(H) G, we have r G + G 2 Let f(x) = x + x 2, f(x) s an ncreasng functon when x 2, and f(2) = 3 4 ; Thus, when G 2, r 3 4, when G =, the source send ts stream drectly to the only vewer, and r =. Theorem 2 apples to any one-vew MPVC. The lower bound of 3/4 s ndependent of the vewng relatons between peers. Ths non-trval lower bound has mportant mplcatons on the practcal mplementaton of MPVC, wthn whch a peer may jon or leave a sub-conference at her wll. It s undesrable to change the vdeo rates of sub-conferences frequently whenever the vewng relatons change. Our results suggest that t s possble to fnd a constant rate for all vdeo sources that s achevable n any possble one-vew MPVC scenaro, ndependent of the vewng relatons among peers, sub-conference szes, and even the total number of peers n the system. We name the maxmum value of such a constant source rate as the guaranteed capacty of one-vew MPVC and denote ths value as C. Theorem 3: If all peers have homogeneous upload bandwdth of, the guaranteed capacty C for any homogeneous one-vew MPVC s 5/6. Proof: In the confguraton of Theorem 2, the vdeo source uses up ts upload bandwdth to dstrbute the vdeo stream to other peers n ts sub-conference. Here we wll use a slghtly dfferent vdeo dstrbuton confguraton to acheve a hgher bound of the capacty C. In ths confguraton, all source peer wll use rate w to upload the ts own vdeo whle the remanng upload bandwdth of w s used to dstrbuton the vdeo t s watchng. On the other hand, dle peers are stll assgned to dfferent sub-conferences n the same way as n Theorem 2. An dle peer wll contrbute ts full upload bandwdth to help transmttng the vdeo assgned to t. Under ths confguraton, besdes the helper bandwdth, the busy peers n G also contrbute upload bandwdth to sub-conference. Accordng to Equaton, v (w) = w + ( w) G(S) + G G B(H) G 2 (5) Case : If all vewers of source are dle peers, and G (S) =. Accordng to the dle peer assgnment rule, =, S. Equaton (5) becomes B (H) v (w) = w + G G To have v (w) w, we need v (w) = w + G G w G ( G )w w

6 6 Snce w <, we always have v (w) w. Thus r = w. Case 2: If exactly one vewer of source s a source, and G (S) =. In ths case, B (H) =. Equaton (5) becomes v (w) = w + ( w) + G G = Thus, Thus r = w n ths case. Case 3: If more than one vewer of source are sources, and G (S) 2. Substtute G (S) = G G I and B(H) = G G (I) nto Equaton (5), we have v (w) = (2 w)( G ) + G 2 + ( w) G + G 2 G (I) Snce G G (S) 2, f we set w = 5 6, when G 4, we have v (5/6) > (2 5/6)( ) (2 5/6)( /4) = 7/8. G when G = 3, we have v (5/6) > (2 5/6)( /3) + /9 8/9 > 5 6 when G = 2, we have v (5/6) > (2 5/6)( /2) + /4 5/6 In all cases, we wll have r 5/6, therefore we conclude that C 5/6. Fnally, to show ths 5/6 s a tght bound of the guaranteed capacty, we only need to come up wth a homogeneous MPVC scenaro such that the maxmal achevable rate on all vdeo sources s only 5/6. Snce we wll use an optmzaton formulaton for the more general heterogeneous MPVC scenaro, we present t as a constructve proof n next paragraph. We construct the followng homogeneous one-vew MPVC: there are sx peers wth unt bandwdth, four of them are sources, S =, 2, 3, 4, the vewng relaton s: G = {3, 4}, G 2 = {6}, G 3 = {, 2},G 4 = {5}. Pluggng n ths scenaro to OPT II, we obtan the max-mn capacty γ = 5/6. Snce all sources have bandwdth, 5/6 s the maxmal achevable rate on all sources. Ths proves that the guaranteed capacty C for any homogeneous onevew MPVC can not be hgher than 5/6. 5 CAPACITY OF HETEROGENEOUS MPVC In the prevous secton, we assume peer upload bandwdth s homogeneous and only assgn dle peers to dfferent sub-conferences. In practce, peer upload bandwdth s heterogeneous. Peer upload bandwdth should be allocated to sub-conferences at fner granularty than. In ths secton, we study optmal peer bandwdth allocaton schemes to acheve dfferent desgn objectves n heterogeneous MPVC systems. 5. Maxmzng Aggregate Vdeo Qualty The frst desgn objectve s to maxmze the total vdeo qualty receved by all peers. We adopt a PSNR-type of vdeo qualty model [4], whch quantfes the qualty of a vdeo stream at rate r as log(r ). The optmal peer bandwdth allocaton s to maxmze the total vdeo qualty of the conference: subject to : OPT I: r u(s) max U,R,B G log(r ), (6) S + j G u (w) j G + B (H) B(H) G 2, (7) r u (s), S (8) u u (s) + u (w) + u (h), S (9) u u (w) + u (h), I () s S B (H) s N u (h), () where (7) and (8) are source vdeo rate constrants accordng to Equaton (), (9) and () are upload bandwdth constrants on sources and dle peers respectvely, and () enforces the bandwdth supply and demand balance n the common helper pool. The objectve functon s a concave functon of {r } and the constrants are all lnear. It s a convex optmzaton problem, for whch effcent centralzed and dstrbuted algorthms can be developed to solve for the optmal vdeo source rates R = {r, S} and the assocated optmal P2P relay scheme characterzed by the peer upload bandwdth allocaton U = {u (s), u (w), u (h), N} and helper bandwdth allocaton B = {B s (H), s S}. Due to the log vdeo utlty functon, the optmal soluton of OPT I acheves the weghted proportonal farness among all vdeo sources, wth the weght for a sub-conference be the number of vewers. 5.2 Achevng Max-Mn Farness Another wdely used farness metrc s the max-mn farness. Intutvely, we prefer all sources to acheve the same rate as long as t s allowed by the ndvdual source s upload capacty and the avalable bandwdth resource n the whole MPVC system. To acheve ths, we want to fnd a vdeo rate γ such that f a vdeo source s upload capacty u s less than γ, t should be able to stream ts vdeo at rate r = u, for any other source wth u γ, t should stream ts vdeo at the common rate r = γ. Under ths settng, the capacty of the system s defned as the maxmal supportable γ, whch can be solved by the followng optmzaton problem. OPT II: max γ (2) U,R,B subject to (7), (8), (9), (), () and a new set of constrants r = mn(γ, u ), S (3)

10 erarchy for bandwdth management. At the top level, a centralzed tracker manages the helper pool shared by all sub-conferences. It keeps track of the bandwdth contrbuted by peers n sub-conferences wth surplus bandwdth, and allocates helper bandwdth to subconferences wth bandwdth defct. At the bottom level, the bandwdth allocaton among peers n each subconference s coordnated by the vdeo source. Source of sub-conference mantans the followng states: ) r : the target vdeo rate for sub-conference ; 2) B (H) : the helper bandwdth borrowed from the helper pool, ntalzed to. 3) A : acheved vdeo rate under current allocaton, ntalzed to. 4) L j : bandwdth on peer j G that has not been allocated, ntalzed to u j. Bandwdth allocaton s carred out n four stages: vdeo source bandwdth allocaton at target rate r ; dle peer bandwdth allocaton; busy peer bandwdth allocaton; bandwdth allocaton to/from helper pool. Bandwdth allocaton n all sub-conferences are coordnated such that bandwdth allocaton n any sub-conference advances to stage k only after all sub-conferences fnsh the allocaton n stage k. Stage : Vdeo source allocates r bandwdth to send out the vdeo stream t produces. The remanng bandwdth of vdeo source s updated as L = u r. Accordng to Equaton (), the acheved vdeo rate s A = r / G. Stage 2: Vdeo source utlze dle vewers bandwdth to grow the achevable vdeo rate from A. The detaled algorthm s shown n Algorthm. Lne pcks up an Algorthm Idle Vewer Bandwdth Allocaton : for each dle peer p G (I) do 2: x = mn ((r A ) G, L p ) 3: A = A + x / G 4: L p = L p x 5: f A r then 6: break 7: end f 8: end for 9: B (H) = B (H) + p G (I) L p dle vewer p n local sub-conference G. Lne 2 uses ths peer s bandwdth to ncrease the vdeo rate. (r A ) G s the amount of bandwdth needed to mprove vdeo rate from A to r. Lne 3 and lne 4 update the acheved vdeo rate A and the unallocated bandwdth L p. Lne 5, 6 and 7 break loop f the target rate r s acheved. Lne 9 allocates the unallocated dle vewer s bandwdth to the helper pool. Stage 3: In ths stage, we allocate the bandwdth on busy peers to sub-conferences n whch the target vdeo rate has not been acheved. Accordng to gudelne G3, a busy peer should frst upload to the smaller subconference between the one she s vewng and the one she s hostng. To acheve ths, we conduct bandwdth allocaton for sub-conferences n the non-decreasng order of ther szes. The bandwdth allocaton wthn each subconference follows Algorthm 2. Ths process allocates Algorthm 2 Busy Vewer Bandwdth Allocaton : for each peer p G (S) {} do 2: x = mn ((r A ) G, L p ) 3: A = A + x / G 4: L p = L p x 5: f A r then 6: break 7: end f 8: end for 9: B (H) = B (H) + p G (S) {} L p bandwdth on the vdeo source of sub-conference and all other vewers who act as vdeo source for other subconferences. The allocaton s smlar to Algorthm and s self-explanatory. Stage 4: In ths stage, a sub-conference that has not acheved ts target rate usng bandwdth on ts source and vewers borrows bandwdth from the helper pool. Accordng to (9) to mprove vdeo rate of G from A to r takng nto account the helper bandwdth overhead, the needed helper bandwdth s B (H) = (r A ) G 2. G Each sub-conference wth bandwdth defct wll request bandwdth B (H) from the common helper pool. In the helper pool, f sum of the requested helper bandwdth s not bgger than the aggregate helper bandwdth B (H) contrbuted by bandwdth surplus sub-conferences, the centralzed tracker wll allocate to each sub-conference the requested helper bandwdth. Otherwse, the targeted vdeo rate vector s not supportable, and the tracker can proportonally reduce the helper bandwdth allocaton to sub-conferences. Through teratve bnary search, the bandwdth allocaton algorthm can also be used to dynamcally approach the max-mn capacty γ defned n OPT II. We frst set the search nterval to be [γ l, γ h ], wth γ h = max s S u s, and γ l beng the lower bounds obtaned n Secton 4 and 5. Specfcally, for a homogeneous MPVC wth normalzed upload bandwdth of, we set γ l = 5 6 ; for a heterogeneous MPVC wth normalzed average upload bandwdth of, we set γ l = max( 2 3, + S ). From the analyss n Secton 4 and 5, the vdeo rate vector determned by γ l : {r s = mn(u s, γ l ), s S}, s always achevable. Usng γ l as the startng pont, we teratvely fnd the maxmal γ that can be acheved by our bandwdth allocaton algorthm. At each teraton, we check whether the vdeo rate vector determned by γ = (γ l + γ h )/2 s achevable. If yes, the search range shrnks to [γ, γ h ]; otherwse,the search range shrnks to

11 [γ l, γ]. Ths process fnshes untl the range s smaller than a pre-defned threshold ɛ. The bnary search pseudocode s presented n Algorthm 3. Algorthm 3 Approachng Capacty through Bnarysearch : procedure MAX-γ(S, N, {u, N}, {G s, s S}) 2: normalze u such that ū = ; 3: f u homogeneous then 4: γ l = 5 6 5: else 6: γ l = max ( ) 2 3, + S 7: end f 8: whle (γ h γ l ) > ɛ do 9: γ (γ h γ l )/2, r s = mn(u, γ), s S : ok=bandwdth-allocaton ({r s, s S}) : f ok== then 2: γ l γ 3: else 4: γ h γ 5: end f 6: end whle 7: return γ 8: end procedure 7 NUMERICAL EVALUATION In ths secton, we present numercal results to demonstrate the tghtness of the derved lower bounds and the effcency of the proposed bandwdth allocaton algorthm. We adopt three types of performance measures. The frst one s the dfference between the acheved vdeo rates and the optmal vdeo rates. The second one s the average vdeo qualty perceved by all users. Usng PSNR vdeo qualty model, the average vdeo qualty s: V = N log(w ) = s S G s log(r s ), (2) where w s the vdeo rate receved by vewer, r s s the vdeo rate of source s, and w = r s, G s. The thrd measure s the bandwdth utlzaton n the conference. Frst of all, the aggregate receved vdeo rate cross all sub-conferences should be less than the sum of upload bandwdth on all peers. Secondly, the vdeo rate of a sub-conference s lmted by the bandwdth of ts vdeo source. Even f there s abundant bandwdth avalable, the aggregate receved vdeo rate n sub-conference hosted by s s lmted by G s u s. We defne the upload bandwdth utlzaton as N B = w mn( N u, s S G (2) s u s ) 7. Homogeneous One-vew MPVC We frst study the tghtness of the derved unversal lower bounds at dfferent system szes by varyng from 6 to 4 wth step-sze 4. For each, we generate γ * Number of vdeo source (a) varyng # of peers Fg. 3. Capacty of Homogeneous MPVC CDF of γ * =6 = = γ * (b) varyng # of sources, random vewng scenaros: we frst select a random number of peers as vdeo sources, then each peer randomly selects a source to watch. For each scenaro, we frst calculate ts max-mn capacty γ usng the optmal algorthm OPT II. The CDF dstrbuton of γ s plotted n Fg. 3(a). The mnmum of γ s 333 5/6. At all system szes, more than 9% scenaros have maxmn capacty greater than.9. Note that the maxmum achevable vdeo rate s at most. Ths ndcates that whle 5/6 s a unversal lower bound ndependent of vewng relatons between peers, for most vewng scenaros, the achevable vdeo rate s pretty close to the upper bound of. As the systems sze grows, less scenaros can acheve the maxmum rate of. For each scenaro, we also use the bnary search algorthm presented Secton. 6.2, denoted as the BA algorthm, to teratvely approach the capacty. We also calculate the dfference between the acheved rate γ by the BA algorthm wth the optmal value γ and fnd the maxmum error s smaller than 3. To nvestgate the mpact of the number of sources, we fx at and vary the number of vdeo sources S from 2 to. For each S, we generate, random vewng scenaros and calculate the max-mn capacty for each scenaro. The results are presented as boxplot n Fgure 3(b). For each S, the central mark n the box s the medan, the edges of the box are the 25th and 75th percentles, the whskers extend to the most extreme data ponts not consdered outlers, and outlers are plotted ndvdually. When S = 2, as proved n Theorem, the maxmal rate of s acheved. As S ncreases, the medan value decreases and the varance ncreases. The lowest medan value and the hghest varance appear at S = 6, where the number of possble vewng scenaros s the largest. As S ncreases further, the medan ncreases and the varance decreases. When S =, each peer s a source and only has one vewer. Each source sends vdeo drectly to her vewer to acheve the maxmum rate of. 7.2 Heterogeneous One-vew MPVC To smulate heterogeneous system, we randomly set peer upload capacty accordng to the dstrbuton lsted n the Table 2, whch s obtaned from a measurement study n [2]. The average peer upload bandwdth s

12 2 TABLE 2 Bandwdth dstrbuton Uplnk(kbps) Probablty class 28.2 class class 3.25 class Mbps. We vary the number of peers from 6 to 2 wth step-sze of 2. For each, we randomly generate 4, vewng scenaros by lettng each peer randomly choose another peer to watch. Totally 6, random vewng scenaros are generated. Fgure 4(a) plots the CDF dstrbuton of max-mn capacty obtaned by OPT II and our bandwdth allocaton algorthm (labeled wth + S )ū BA). We also plot the lower bound of max ( 2 3, for each scenaro. We can see that the BA curve s very close to the OPT II curve. Ths suggests that the BA algorthm s very effcent n approachng the maxmn capacty bound n heterogeneous systems. In the fgure, there s a large gap between the max-mn capacty and lower bound. Ths s because the lower bound s ndependent of vewng scenaros and s always below peer s average upload bandwdth. But OPT II and BA algorthms work on specfc vewng scenaro, and the obtaned γ reflects the obtaned maxmal vdeo source rate, whch can go well beyond the average upload rate f a vdeo source wth hgh upload bandwdth has just one or few vewers. In Fgure 4(a), we also plot the average vewng rate among all peers. In addton to OPT II, BA and the lower bound, we also consder OPT I defned n (6), the bandwdth allocaton optmzed drectly for vdeo qualty. The average curves of OPT I, OPT II, and BA algorthm are clustered together, and the gap between them and the average rate curve of the lower bound s smaller than the max-mn capacty gap. Fgure 4(b) shows the relatve performance dfference of OPT I, BA algorthm and lower bound compared wth OPT II (the relatve dfference between x and y s defne as x y y ). We frst consder the max-mn capacty obtaned by BA. By the curve labeled as γ of BA, the BA algorthm can acheve 93% of optmal max-mn capacty wth 9% probablty. For the average vewng rate, the dfference between the BA algorthm and OPT II s farly small. Snce OPT I s optmzed for the vdeo qualty, the average rate obtaned by OPT I can be hgher than OPT II. The relatve performance of the lower bound s the worst. The average rate of the lower bound s wthn 75% of OPT II wth 8% probablty. Fgure 4(c) plots the average vdeo qualty V obtaned by dfferent algorthms. The curve of OPT I, OPT II and BA algorthms are almost dentcal. The performance of the lower bound s worse than the other three algorthms, wth the relatve dfference less than 8%. Fnally, Fgure 4(d) compares the peer bandwdth utlzaton B as defned n (2). The utlzaton of OPT I, OPT II, TABLE 3 Heterogeneous MPVC, Random Sources =6 =8 = =2 S =2.6838/ S =3.776/ / S =4 255/ /693.77/54 - S =5 98/ 222/ / /66 S =6./78 6/277 48/43 9/387 S = /28 483/442 79/22 S = / /432 S = /6 7/777 S = /9 S = /2 BA algorthm are all very close to one. Ths suggests that those algorthms have effcently utlzed upload bandwdth avalable on sources and vewers to acheve hgh vdeo rates, and there s not much space for further qualty mprovement. But for the lower bound curve, snce t s not optmzed for specfc vewng scenaro, the bandwdth utlzaton s stll far from the perfect case. Ths suggests that the space for bandwdth allocaton optmzaton for ndvdual vewng scenaros s often necessary and rewardng. To nvestgate the mpact of and S, we cluster 6, random vewng scenaros based on the, S tuple. For each scenaro, we normalze the average vdeo vewng rate wth the average upload bandwdth. For each N, S cluster, we calculate the mean of the normalzed average vewng rate for all scenaros n that cluster. Table 3 presents results for N, S clusters wth at least 2 random scenaros. For each tem of the table, left number represent the mean of the normalzed average vewng rate and rght number represent the number of samples. the Each column corresponds to one system sze. Dfferent from the homogeneous case, at all smulated system szes, the average vdeo rate ncreases as the number of sources ncreases. Ths s because the acheved vdeo rate n each sub-conference s lmted by both the source upload bandwdth and the bandwdth avalable to ths sub-conference. When the number of sources s smaller, each source wll have more vewers. If a weak peer s chosen as a source, t wll degrade the vdeo qualty on more peers. Consequently, the acheved average vdeo rate wll be lower. To elmnate the mpact of weak sources, we repeat the prevous experments wth an addtonal requrement that each source must have upload bandwdth larger than the average bandwdth. Specfcally, we frst generate the peer bandwdth accordng to Table 2, choose only peers wth bandwdth larger than the average bandwdth as sources, then let each peer randomly choose a source to watch. Accordng to Corollary 2, we now use max ( 3 4, + S )ū as the lower bound. The results are plotted n Fg. 5. When we requre all sources have

13 3 CDF of average vdeo rate.6 γ * of BA lower bound OPT II.4 Ave BA.2 Ave lower bound Ave OPT I Ave OPT II Average vdeo rate(kbt/s) CDF of relatve dfference γ * of BA lower bound Ave BA Ave OPT I Ave lower bound.5.5 Relatve dfference CDF of vdeo qualty.6.4 OPT II lower bound.2 BA OPT I Vdeo qualty CDF of bandwdth utlzaton optmal II lower bound BA opt I Bandwdth utlzaton (a) acheved vdeo rates (b) relatve performance (c) average vdeo qualty (d) bandwdth utlzaton Fg. 4. Performance of Heterogeneous MPVC wth 6, random vewng scenaros. CDF of average vdeo rate.6.4 Ave BA Ave lower bound.2 Ave OPT I Ave OPT II Average vdeo rate(kbt/s) (a) acheved vdeo rates CDF of relatve dfference Ave BA Ave OPT I Ave lower bound.5.5 Relatve dfference (b) relatve performance CDF of average vdeo qualty OPT II lower bound BA OPT I Average vdeo qualty (c) average vdeo qualty CDF of bandwdth utlzaton optmal II lower bound BA opt I.7.9 Bandwdth utlzaton (d) bandwdth utlzaton Fg. 5. Performance of Heterogeneous MPVC when each vdeo source s bandwdth s larger than the average bandwdth. capacty hgher than the average upload bandwdth, the source uplnk wll no longer be the bottleneck. To acheve the max-mn farness, all sub-conferences wll acheve the same rate. So the max-mn capacty acheved by OPT II s exactly the same as the average vewng rate of all peers. In Fg. 5(a), we only plot the average rates acheved by dfferent algorthms. If we compare Fg. 4(a) and 5(a), we do acheve hgher average vewng rates when all sources are bandwdth-rch. But the correspondng max-mn capacty γ s lower than those acheved n Fgure 4(a). Ths s because when there s no requrement on source bandwdth, sub-conferences hosted by weak sources are lmted by source upload bandwdth, strong sources can potentally acheve hgher rates and push up the max-mn capacty γ. Fg. 5(b) plots the relatve performance on the average rate of BA, OPT I and lower bound compared wth OPT II. Fg. 5(c) compares the average vdeo qualty acheved by dfferent algorthms. In Fg. 5(a), 5(b) and 5(c), the new lower bound curves are closer to OPT and BA curves than n Fg. 4(a), 4(b) and 5(c). Comparng Fg. 4(d) and 5(d), bandwdth utlzaton mproves when sources are no longer bottleneck. The lower bound curve n Fg. 5(d) s pece-wse constant. Ths s because the bandwdth + S ). utlzaton defned n (2) s now exactly max ( 3 4, For the smulated scenaros, there are only lmted number of, S tuples satsfyng + S > 3 4, e.g, 8, 2,, 3, etc., leadng to fve dscrete values of B. Fnally, we revst the mpact of the number of sources when sources are bandwdth-rch. As presented n Table 4, opposte to Table 3, at all smulated system szes, when the number of sources ncreases, the vdeo rate decreases. TABLE 4 Heterogeneous MPVC, Strong Sources =6 =8 = =2 S =2.9992/ / / /326 S =3.988/34.979/ /37.992/223 S =4.9546/25.972/ /75.977/826 S =5.955/29.95/84.956/ /48 S = /4.9393/7 S = /33 Ths s because when the sources are no longer the bottleneck, the acheved vdeo rate n a sub-conference s only determned by the bandwdth avalable to ths sub-conference. When the number of sources s larger, the number of peers n each sub-conference s smaller. Wth heterogeneous peer upload bandwdth, the average bandwdth wthn each sub-conference has larger varance. Sub-conferences wth less bandwdth have to borrow bandwdth from the helper pool and ncur helper bandwdth overhead. Consequently the acheved vdeo rate decreases. 7.3 Helper Overhead of BA Algorthm The desgn objectve of the BA algorthm s to acheve target vdeo rates wth mnmum peer upload bandwdth. The major consderaton of the BA desgn gudelnes n Secton 6 s to maxmally avod helper bandwdth overhead. In ths secton, we study the helper bandwdth overhead ncurred by our BA algorthm. We defne the aggregate helper bandwdth overhead rato

15 5 [] Y. Huang, T. Z. Fu, D.-M. Chu, J. C. Lu, and C. Huang. Challenges, desgn and analyss of a large-scale p2p-vod system. In Proceedngs of the ACM SIGCOMM, 28. [] R. Kumar and K. Ross. Optmal Peer-Asssted Fle Dstrbuton: Sngle and Mult-Class Problems. In IEEE HOTWEB, 26. [2] J. Lennox and H. Schulzrnne. A Protocol for Relable Decentralzed Conferencng. In NOSSDAV, 23. [3] J. L, P. A. Chou, and C. Zhang. Mutualcast: An Effcent Mechansm for Content Dstrbuton n a P2P Network. In Sgcomm Asa Workshop, 25. [4] C. Lang, M. Zhao, and Y. Lu. Optmal Bandwdth Sharng n Mult-Swarm Mult-Party P2P Vdeo Conferencng Systems. IEEE/ACM Transactons on Networkong, 9(6), 2. [5] C. Luo, W. Wang, J. Tang, J. Sun, and J. L. A Multparty Vdeo Conferencng System over an Applcaton-Level Multcast Protocol. In IEEE Transactons on Multmeda, volume 9, pages , 27. [6] M. Ponec, S. Sengupta, M. Chen, J. L, and P. Chou. Mult-rate Peer-to-Peer Vdeo Conferencng: A Dstrbuted Approach Usng Scalable Codng. In Proceedngs of ICME, 29. [7] PPLve. Homepage. [8] S. Sen and J. Wang. Analyzng peer-to-peer traffc across large networks. In IEEE/ACM Transactons on Networkng, volume 2, pages , 22. [9] Skype. Homepage. [2] Y. Xu, C. Yu, J. L, and Y. Lu. Vdeo Telephony for End-consumers: Measurement Study of Google+, Chat, and Skype. In Proceedngs of Internet Measurement Conference, November 22. poly.edu/faculty/yonglu/docs/mc2tech.pdf. Changja Chen s a Full Professor wth Bejng Jaotong Unversty. He receved hs M.S. degree from the Electroncs Insttute of Chnese Academy n 982 and Ph.D degree from Unversty of Hawa n 986, respectvely. Hs general research nterests nclude modelng, desgn and analyss of communcaton networks. Hs current research nterests nclude Peer-to-Peer systems, overlay networks, and network measurement. Yongxang Zhao s an assocate professor wth the Electrcal and Informaton Engneerng School at the Bejng JaoTong Unversty (BJTU),Chna He joned BJTU as an assstant professor n March, 22. He receved hs Ph.D. degree from Electrcal and Informaton Engneerng School at BJTU, n March 22. He receved hs master and bachelor degrees n the feld of Communcaton and Electronc system from BJTU, n 992 and 998, respectvely. Hs general research nterests nclude modelng and desgn communcaton networks. Hs current research nterests nclude Peer-to-Peer systems, overlay networks, and cloud computng. Yong Lu s an assocate professor at the Electrcal and Computer Engneerng department of the Polytechnc Insttute of New York Unversty (NYU-Poly). He joned NYU-Poly as an assstant professor n March, 25. He receved hs Ph.D. degree from Electrcal and Computer Engneerng department at the Unversty of Massachusetts, Amherst, n May 22. He receved hs master and bachelor degrees n the feld of automatc control from the Unversty of Scence and Technology of Chna, n July 997 and 994 respectvely. Hs general research nterests le n modelng, desgn and analyss of communcaton networks. Hs current research drectons nclude Peer-to-Peer systems, overlay networks, network measurement, onlne socal networks, and recommender systems. He s the wnner of ACM/USENIX Internet Measurement Conference (IMC) Best Paper Award n 22, IEEE Conference on Computer and Communcatons (INFOCOM) Best Paper Award n 29, and IEEE Communcatons Socety Best Paper Award n Multmeda Communcatons n 28. He s a member of IEEE and ACM. He s currently servng as an assocate edtor for IEEE/ACM Transactons on Networkng, and Elsever Computer Networks Journal. Janyn Zhang s a network research project manager n the Department of Network Technology at Chna Moble Communcatons Corporaton Research Insttute(CMCC). He joned CMCC as a project manager n August 28. He receved hs Ph.D. degree from Computer Scence and Technology School at BUPT n July 27. He receved hs master degree n the feld of Computer Communcaton from Chna Electroncs Technology Group Corporaton 54 th Research Insttute n March 22. He receved hs bachelor degree n the feld of mcroelectroncs from Xdan Unversty n July 997. Hs general research nterests nclude feature nteracton, new generaton Internet and telecom servces, new generaton telecom network, and network vrtualzaton. Hs current research s focused on Peer-to-Peer systems, multmeda servces, IPTV and OTT vdeo, and Web real-tme communcaton. He has served as an edtor of Q9 n the ITU-T SG3 from 29 to 2.

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