Adaptive Voice Smoother with Optimal Playback Delay for New Generation VoIP Services

Transcription

1 Adaptve Voce Smoother wth Optmal Playack Delay for New Generaton VoIP Servces Shyh-Fang Huang 1, Erc Hsao-Kuang Wu 2, and Pao-Ch Chang 3 1 Department of Electrcal Engneerng, Natonal Central Unversty, Tawan, hsf@vapla.ee.ncu.edu.tw 2 Department of Computer Scence & Informaton Engneerng, Natonal Central Unversty, Tawan, hsao@cse.ncu.edu.tw 3 Department of Communcatons Engneerng, Natonal Central Unversty, Tawan, pcchang@ee.ncu.edu.tw Astract. Perceved voce qualty s a key metrc n VoIP applcatons. The qualty s manly affected y IP network mparments such as delay, jtter and packet loss. Playout uffer at the recevng end can e used to compensate for the effects of jtter ased on a tradeoff etween delay and loss. Adaptve smoothng algorthms are capale of adjustng dynamcally the smoothng tme ased on the network parameters to mprove voce qualty. In ths artcle, we ntroduce an effcent and easy perceved qualty method for uffer optmzaton to archve the est voce qualty. Ths work formulates an onlne loss model whch ncorporates uffer szes and apples the Lagrange multpler approach to optmze the delay-loss prolem. Dstnct from the other optmal smoothers, the proposed optmal smoother s sutale for any codec and carres the lowest complexty. Smulaton experments valdate that the proposed adaptve smoother archves sgnfcant mprovement n the voce qualty. 1 Introducton The rapd progress of the development of IP-ase network has enaled numerous applcatons that delver not only tradtonal data ut also multmeda nformaton n real tme. The next generaton network, lke an ALL-IP network, s a future trend to ntegrate all heterogeneous wred and wreless networks and provde seamless worldwde molty. In an All-IP network, one revoluton of the new generaton Internet applcatons wll realze VoIP servces that people can talk freely around through the mole-phones, the desktops and VoIP telephones at any tme and place. Unfortunately, the IP-ased networks do not guarantee the avalale andwdth and assure the constant delay jtters (.e., the delay varance) for real tme applcatons. In other words, ndvdual transmsson delays for a gven flow of packets n a network may e contnung to change caused y varyng traffc load and dfferng routng paths due to congestons, so that the packet network delays for a contnuous seres of ntervals (.e.

2 talkspursts) at the recever may not e the same (.e. constant) as the sender. In addton, a packet delay may ntroduce y the sgnal hand-out or the dfference of andwdth transportaton n wreless/fxed networks. For delay senstve applcatons, a domnatng porton of packet losses mght e lkely due to delay constrant. A late packet that arrves after a delay threshold determned y playack tme s treated as a lost packet. A tght delay threshold not only degrades the qualty of playack ut also reduces the effectve andwdth ecause a large fracton of delvered packets are dropped. In fact, delay and loss are normally not ndependent of each other. In order to reduce the loss mpact, a numer of applcatons utlze an adaptve smoothng technque n whch uffers are adopted to reduce the qualty damage caused y loss packets. However, a large uffer wll ntroduce excessve end-to-end delay and deterorate the multmeda qualty n nteractve realtme applcatons. Therefore, a tradeoff s requred etween ncreased packet loss and uffer delay to acheve satsfactory results for playout uffer algorthms. In the past, the works on the degradaton of the voce qualty consder the effect of packet loss, ut not that of packet delay. Wthn lterature on ctng delays, the use of Pareto dstruton n [1] s of computng the dstruton parameters and reuldng the new dstruton to ct the next packet delay, and the use of neural network models to learn traffc ehavors [2]. The use of Pareto dstruton or a neural network model requres relatvely hgh complexty or a long learnng perod. Therefore, we consder the smoothers [3]-[9] whch employ statstcal network parameters related wth the voce characterstc,.e. loss, delay and talk-spurt that have sgnfcant nfluence to the voce qualty. They detect delay spke n traffc and quckly calculate the requred uffer sze to keep the qualty as good as possle. For perceptual-ased uffer optmzaton schemes for VoIP, voce qualty s used as the key metrc ecause t provdes a drect lnk to user perceved QoS. However, t requres an effcent and accurate ojectve way to optmze perceved voce qualty. In ths paper, we propose a new delay-loss smoother that employs the Lagrange multpler method to optmze the voce qualty y alancng the delay and the loss. Lagrange multpler method s often used to optmze the trade off prolems. The contrutons of ths paper are two-fold: () A new method s for optmzng voce qualty for VoIP and s easly appled to new codecs. () Dfferent from the other optmal smoothers, our optmal smoother has the lowest complexty wth O ( n). The remander of the paper s structured as follows. Secton 2 revews the prevous work. Secton 3 proposes the novel adaptve smoother. Secton 4 shows the smulaton results n smoothers. Fnally, conclusons are provded. 2 Related Work The SD algorthm has een studed y many researchers [3]-[9]. A delay spke s defned as a sudden and sgnfcant ncrease of network delay n a short perod often less than one round-trp. Ths algorthm adjusts the smoothng sze,.e. playack delay, at the egnnng of each talk-spurt. The results of ths algorthm are therefore compared to the results otaned heren.

3 The SD Algorthm n [3] estmates the playout tme p of the frst packet n a talkspurt from the mean network delay d and the varance v for packet as p = t + d + γ v (1) where t represents the tme at whch packet s generated at the sendng host and γ s a constant factor used to set the playout tme to e far enough eyond the delay estmate such that only a small fracton of the arrvng packets could e lost due to late arrval. The value of γ = 4 s used n smulatons [3]. The estmates are recomputed each tme a packet arrves, ut only appled when a new talk-spurt s ntated. The mean network delay d and varance v are calculated ased on a lnear recursve flter characterzed y the factors α and β. If n > d If n d -1-1 d = d v = v d = d v = v + (1 )n + (1 )d + (1 )n + (1 )d n n (SPIKE_MODE) where n s the end-to-end delay ntroduced y the network and typcal values of α and β are and 0.75 [3], respectvely. The decson to select α or β s ased on the current delay condton. The condton n > d represents network congeston (SPIKE_MODE) and the weght β s used to emphasze the current network delay. On the other hand, n d represents network traffc s stale, and α s used to emphasze the long-term average. In estmatng the delay and varance, the SD Algorthm uses only two values αand β that are smple ut may not e adequate, partcularly when the traffc s unstale. For example, an under-estmated prolem s when a network ecomes spked, ut the delay n s just elow the d, the SD Algorthm wll judge the network to e stale and wll not enter the SIPKE_MODE. (2) 3 Optmal Smoother wth Delay-Loss Trade off The proposed optmal smoother s derved usng the Lagrangan method to trade off the delay and loss. Ths method nvolves, frst, uldng the traffc delay model and the loss model. Second, a Lagrangan cost functon Q s defned usng ths delay and the loss models. Thrd, the Lagrangan cost functon Q s mnmzed and thus the delay and loss optmzed soluton s otaned.

4 3.1 Traffc Delay and Loss Models f (t) Smoothng tme Loss Delay t Fg. 1. The relaton of smoothng delay and loss For perceved uffer desgn, t s crtcal to understand the delay dstruton modelng as t s drectly related to uffer loss. The characterstcs of packet transmsson delay over Internet can e represented y statstcal models whch follow Exponental dstruton for Internet packets (for a UDP traffc) has een shown to consstent wth an Exponental dstruton [10]. In order to derve an onlne loss model, the packet end-to-end delay s assumed as an exponental dstruton wth parameter 1 µ at the recevng end for low complexty and easy mplementaton. The CDF of the delay dstruton F ( t ) can also e represented y [11] and the PDF of the delay dstruton f (t ) s f ( t ) tu F ( t ) = 1 e (3) = df( t ) dt tµ = µ e In a real-tme applcaton, a packet loss that s solely caused y extra delay can e derved from the delay model f (t ). Fgure 1 plots the delay functon f (t ), whch shows that when the packet delay exceeds the smoothng tme; the delayed packet s regarded as a lost packet. The loss functon l ( ) can e derved from Fg. 1 as µ t µ µ ( ) f ( t )dt = ( e ) = e + e = e l = From Eqs. (4) and (5), we otan the delay and loss functons that wll e used n Lagrangan cost functon. (4) (5)

5 3.2 Optmal Delay-Loss Adaptve Smoother To express the correspondng qualty for a gven voce connecton, a Lagrangan cost functon Q s defned ased on the delay and the loss model l ( ) Q ( ) = + K l( ) (6) where Q ( ) represents the negatve effect on voce qualty,.e., mnmzng Q yelds the est voce qualty. K s a Lagrange multpler where the loss ecomes more sgnfcant as K ncreases. The K value has sgnfcant nfluence on the optmzaton process. We wll dscuss the vald range of the value n ths secton and the suggested value n the next secton. µ Here, once a smoothng tme s specfed, the loss l( ) = e can e calculated from Eq. (5). The Lagrangan cost functon n Eq. (6) yelds The dfferental equaton the smoothng tme, ( ) = + K e Q µ (7) dq d s assgned to zero that mnmzes Q to yeld ( ) = µ ln Kµ (8) max where s the est smoothng tme for alancng the delay and the loss. Afterwards, the smoother can provde est qualty, consderng oth the delay and the loss effects, ased on the calculated smoothng tme. The calculated smoothng tme s a functon of K and µ. µ denotes a IPase network delay parameter (end-to-end delay) and can e measured at the recever, ut K s gven y users or applcatons. The calculated smoothng tme must e wthn an allowale range to ensure that the end-to-end delay s acceptale. Here, D s defned as the maxmum acceptale end-to-end delay and the calculated smoothng tme must e etween 0 and D max ( K ) Dmax 0 = µ ln µ (9) Accordngly, the permssle range of vald K n the Lagrange multpler Q functon n Eq. (8) s µ K µ e D max µ (10) 3.3 Suggeston of K Parameter In ths secton the relatonshp etween the voce qualty and loss s further studed. Based on the prevous secton dscussons, we know K parameter s tghtly related wth voce qualty. In other words, for a gven MOS (Mean Opnon Score) of

6 speech qualty, the allowale range of K can further e restrcted. Many studes revealed the dffculty of determnng the mathematcal formula that relates the voce qualty, delay, and loss. Accordng to [12], the loss degrades the voce qualty more remarkaly than does the delay, so the qualty-loss relatonshp s frst emphaszed [13] [14]. In these studes, an emprcal Eq. (11) was otaned y experments wth many traffc patterns for ctng the voce MOS qualty MOS that mght e degraded y the traffc loss ( loss ) where ( +1) MOS = MOS opt c ln loss (11) MOS opt s voce codec related, representng the optmum voce qualty that the codec can acheve, c s a constant that s codec dependent, and loss s a percentage rato tmes 100. Followng ths approach, anyone can estmate a specfc emprcal rule wth specfed voce codecs and network envronments. Equaton (11) also mples that the network loss rate must e kept lower than or equal to the defned loss to ensure the cted MOS MOS. Equaton (11) s rewrtten to yeld Eq. (12), MOS opt MOS loss = 2 c Notaly, the l (t ) functon s a percentage ut loss s not. Therefore, l (t ) s multpled y 100 to yeld loss = 2 MOS opt MOS c l( t ) = e From Eq. (13), the smoothng tme s ln MOS opt 2 c MOS 100 µ µ From Eqs. (8), (10) and (14), the suggested range for K s K max ( 2 MOS 100 µ opt MOS c, µ ) When K s assgned a value that s more than the threshold n Eq. (15), the desgn of the smoother s manly domnated y the loss effect. For a gven MOS, a sutale K can e suggested and an optmal uffer sze can e determned. (12) (13) (14) (15)

7 4. Smulaton 4.1 Smulaton Confguraton A set of smulaton experments are performed to evaluate the effectveness of the proposed adaptve smoothng scheme. The OPNET smulaton tools are adopted to trace the voce traffc transported etween two dfferent LANs for a VoIP envronment. Nnety personal computers wth G.729 traffcs are deployed n each LAN. The duraton and frequency of the connecton tme of the personal computers follow Exponental dstrutons. Ten fve-mnute smulatons were run to proe the ackone network delay patterns, whch were used to trace the adaptve smoothers and compare the effects of the orgnal wth the adapted voce qualty latter. *90 Router T1 Router *90 Fg. 2. The smulaton envronment of VoIP Tale 1. Smulaton parameters Attrute Value Numers of n one LAN 90 s Codec G.729 Backone T1 (1.544 Mps) LAN 100 Mps Propagaton delay Constant Router uffer Infnte Packet sze 50 ytes Fg. 2 shows the typcal network topology n whch a T1 (1.544 Mps) ackone connects two LANs, and 100 Mps lnes are connected wthn each LAN. The propagaton delay of all lnks s assumed to e a constant value and wll e gnored (the dervatve value wll e zero) n the optmzaton process. The uffer sze of the ottlenecked router s assumed to e nfnte snce the performance comparson of adaptve smoothers wll e affected y overdue packet loss (over the deadlne) and not affected y the packet loss n router uffer. The network end-to-end delay of a G.729 packet wth data frame sze (10 ytes) and RTP/UDP/IP headers (40 ytes) s measured for ten fve-mnute smulatons y employng the OPNET smulaton network. Tale 1 summarzes the smulaton parameters. Fgure 3(a) and 3() lst one

8 of the end-to-end traffc delay patterns and the correspondng delay varances for VoIP traffc oserved at a gven recever delay (ms) 400 Varance Packet Numer (a) The delay of traffc Fg. 3. VoIP traffc pattern Talk Spurt () The varance of traffc 300 Smoothers Ave. Delay SD 0.6 smoothers SD Optmal Optmal 200 Delay (ms) Late Rate (%) Talkspurt Fg. 4 The ctng tme of smoothers Talk Spurt Fg. 5 The packet loss rate of smoothers 4.2 Predcted Smoothng Tme and Loss Rate n Smoothers In ths secton the accuracy of the cted end-to-end delay tme and loss rate among these smoothers are compared. The maxmum acceptale delay D max s set to 250 ms and the average delay s used to oserve the traffc pattern n partcular. In Fg. 4 and Fg. 5, we can oserve that the cted tme of the SD smoother s very close to the mean delay and the loss rate s hgher than optmal smoother. The SD smoother uses a large value of fxed β to deal wth varous traffc condtons and emphasze a long-term mean delay 1 d, so the cted delay wll e close to the mean delay. A etter choce for n s proaly the maxmum delay n the last talkspurt that can suffcently represent the worst case of current network congeston and avod an under-estmated delay.

9 4.3 Qualty Measurement The test sequence s sampled at 8 khz, seconds long, and ncludes Englsh and Mandarn sentences spoken y male and female. Tale 2 lsts the mean delay, mean loss rate, and SSNR measured n a voce qualty test wth varous smoothers. SSNR [15][16] s used as an evaluaton tool ecause t correlates etter wth MOS and t s relatvely smple to compute. Tale 2 shows that the Optmal smoother performance acheves a hgh average SSNR and has the sgnfcant mprovement n the voce qualty over SD smoother, snce the proposed optmal smoother truly optmzes wth the delay and loss mparments. The SSNR can only represent the loss mpact, ut hardly represent the delay mpact. Therefore, a Lagrangan cost functon s utlzed to consder the delay and loss mpacts to the qualty degradaton for varous smoothers. In order to mantan the normal voce qualty over the network, the cted MOS, MOS = 3 s requred. Accordng to [14] and G.729, c s set as 0.25 n formula (15) and the µ s set as the frame rate 10 ms for G.729 at the sender. The Lagrange multpler value K = 430 s calculated from the formula (15). Tale 3 shows that the optmal smoother has the lower Lagrangan cost value than SD smoother. Specfcally, we can oserve the optmal smoother has 23% mprovement of the qualty degradaton on SD smoother. Tale 2. The voce qualty test of smoothers Source SD Optmal SSNR (db) Mean delay (ms) Mean Loss Rate (%) Tale 3. The mean cost value of smoothers at hgh traffc load Smoother SD Optmal Lagrangan Cost (ms) 5. Concluson For new-generaton VoIP servces, a dynamc smoothng algorthm s requred to address IP-ased network delay and loss. Ths work proposes an optmal smoothng method to otan the est voce qualty y Lagrangan lost functon whch s a trade off etween the negatve effects of the delay and the loss. It can effcently solve the msmatch etween the capture and the playack clocks. Numercal examples have shown that our proposed method can control the playout tme to alance the target delay and loss.

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