A heuristic approach of bandwidth management for video sources in ATM networks

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENTInt. J. Network Mgmt 2000; 10:41–49

A heuristic approach of bandwidth management forvideo sources in ATM networks

By Yen-Wen ChenŁ and Jean-Lien C. Wu†

One of the most important properties in the ATM network is that theresource of the network, including buffer and bandwidth, can be flexiblymanaged according to different demands of various applications. Thenetwork bandwidth can be effectively allocated and utilized if the datavolume of the arrival traffic can be predicted precisely. In this paper, westudy the bandwidth management schemes for variable bit rate (VBR)pre-coded MPEG video sources. The proposed bandwidth allocationmethod, which predicts the bandwidth by the frame correlation,demonstrates a quite good performance when comparing with a previousscheme, especially for the video scenes with the combination of intraframesand interframes. Bandwidth allocation of a multiplexer connected toseveral video sources is also studied by using heuristic information. Theexperimental results show that the proposed method is much better thanthat of the fixed bandwidth allocation and is suitable for the applicationof MPEG video services. Copyright 2000 John Wiley & Sons, Ltd.

Introduction

A mong the various kinds of services pro-vided by broadband integrated servicesdigital networks (BISDN), video service

is emerging more and more for the futuremultimedia environment.1,2 In order to utilize thenetwork bandwidth effectively, frames of videosources are always encoded with variable trafficvolume.3 – 5 For example, the Moving Picture Expert

Yen-Wen Chen received the B.E. degree in electrical engineering from Tatung Institute of Technology, ROC, in 1981, the M.S. degreein information engineering from National Central University, in 1983, and the Ph.D degree in electronic engineering from NationalTaiwan University of Science and Technology (NTUST) in 1995. In 1983, he joined the Department of Switching Technology, ChunghuaTelecommunication Laboratories, Taiwan, ROC, as an Assistant Researcher. From 1995 to 1998, he was a project manager of broadband switchsystems to develop the permanent virtual connection (PVC) broadband ATM switching systems. This system has been installed in several sites inTaiwan. In August 1998, he joined the Department of Information Management, National Central Police University, as an Assistant Professor.His current research interests include traffic modelling, broadband internet architectures, network management, and network security. Dr Chenis a member of the IEEE communication society. E-mail: [email protected]

Jean-Lien C. Wu received the BSEE from the National Taiwan University in 1970, the MSEE from the University of Tennessee in 1972,and the Ph.D. degree in EE from Cornell University, Ithaca, NY, in 1976. She worked as a research associate at Cornell University from1976 to 1978. From 1978 to 1984, she was with the Bell Laboratories, Holmdel, NJ, as a member of technical staff, where she was engaged invarious areas such as network management and planning, traffic engineering, and the design and development of business switching systems.From August 1984, she joined the Department of Electronic Engineering, National Taiwan University of Science and Technology (NTUST),Taiwan, ROC, as a Professor. She was head of the department from 1987 to 1990. She is now Dean of the College of Electrical and InformationEngineering, NTUST. Her current research interests are traffic modelling for broadband networks and the Internet, performance evaluation formobile networks, multimedia networking and high-speed transport systems. Dr Wu is a member of the IEEE communication society as well asthe computer society.

ŁCorrespondence to: Yen-Wen Chen, Department of Information Management, National Central Police University, No. 56 Shu-Zen Road,

Da-Kang Thuen, Kwi-Shan Hsiang, Tao-Yuan Hsieng, Taiwan.

†Current address: Department of Electronic Engineering, National Taiwan University of Sciences and Technology, Taiwan.

Copyright 2000 John Wiley & Sons, Ltd. CCC 1055–7148/2000/010041–09$17.50

42 YEN-WEN CHEN AND JEAN-LIEN C. WU

Group (MPEG) defines standards for video com-pression for a wide range of applications. Amongvarious communication techniques, asynchronoustransfer mode (ATM) technique is known to havethe capability of providing flexible bandwidth allo-cation so that the bandwidth can be effectively usedand the required quality of services (QoS) can bemaintained at an acceptable level. This attractiveproperty is very suitable for variable bit rate (VBR)video communication. A number of papers6 – 10

worked on this topic, however, most of them focuson the management of general traffic7,8 and cannoteasily be applied to video sources. Pancha et al.6

proposed a simple prediction method, which wasbased on the information of the mean frame size,bandwidth of previous frames and the standarddeviation of the frame size, for the bandwidth allo-cation of a single video source. Chong proposed theN-order autoregressive (AR) model by using theneural network approach to predict the requiredbandwidth dynamically;9 however, the computa-tion is quite complicated. In this paper, a heuristicapproach is proposed to predict pre-coded MPEGvideo traffic volume and then to allocate the net-work bandwidth based on the correlation betweenconsecutive frames. The experimental results illus-trate that the proposed method demonstrates aquite good accuracy of bandwidth prediction whencomparing to which proposed in.6

The paper is organized as follows. In Section 2,the basic traffic characteristic of the MPEG videosource is reviewed. The basic concept of theproposed bandwidth prediction scheme and itsapplication to single video source are illustratedin Section 3. A more heuristic bandwidth manage-ment method for a limited bandwidth multiplexerconnected with multiple video sources is describedin Section 4. And the conclusion is provided in thelast section.

Overview of MPEG VideoCharacteristics

Basically, a MPEG video source consists ofseveral-coded video frames.11 Frames in MPEGcoding can be divided into intraframes and inter-frames. The intraframe is an independent codingwhich means no reference to other frames isrequired, while the coding of the interframe shall

refer to previous frames and compute the differ-ence. The types of frames can be grouped into Iframes, P frames, and B frames, where I frames areintraframes and P and B frames are interframes,for satisfying the requirements of high compres-sion ratio and random access. The P frame is theforward prediction frame while the B frame isthe bi-directional prediction frame as shown inFigure 1.

A video scene can contain only I frames or themixture of different frame types with a periodi-cal frame sequence structure. The video encoderdetermines the frame sequence and the ratio ofinterframes to intraframes. The frame sizes of dif-ferent frame types are quite different. For example,the characteristics of two MPEG video sources, the‘Black and White’ (Source 1) contains only I frames,and the ‘Red’s Nightmare’ (Source 2) which con-tains mixed frame types, are shown in Figure 2.Figure 2(a) shows the frame size of the consec-utive 550 frames of Source 1; and Figure 2(b) to(d) illustrate the I, P, B frame size, respectively,of Source 2. It is obvious that the frame sizes aredifferent for different frame types. The character-istics of video traffic differ from those of othertraffic in the large data volume deviation and thehigh correlation. Therefore, it is very importantand necessary to consider these properties whenperforming network bandwidth management forvideo traffic. Basically, histograms and the auto-correlation are two of the most important statisticswhen characterizing the correlation between videoframes.4,5,13

Forward Prediction

1 2 3 4 5 6 7 8 9

I B B B B B B P

Bidirectional Prediction

P

Figure 1. Relationships of I, P and B frames

Copyright 2000 John Wiley & Sons, Ltd. Int. J. Network Mgmt 2000; 10:41–49

VIDEO SOURCES IN ATM NETWORKS 43

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Figure 2. The frame sequences of the ‘Black and White’ and ‘Red’s Nightmare’ video sources

Heuristic Bandwidth PredictionScheme

As mentioned in the previous section, the framesize of a video source depends not only on thecontent of the source but also the frame type. Thebandwidth prediction method proposed in refer-ence 6 only considered the statistic characteristics,mean size and standard deviation, of the videosources; frame correlation was not considered. Inthis section, the prediction method proposed in ref-erence 6 is extended to take the correlation propertyof consecutive video frames into consideration.

The bandwidth prediction method proposed issimilar to that proposed in reference 6, which

selects the minimum value between the meanframe size and the sum of the previous frame andstandard deviation as the predicted size of the nextframe. But instead of using the standard deviation,we use the correlation among frames and the frametype by considering the frame size autocorrelationwith one frame lag and the normalization factorfor different frame type. For scenes with differentframe types, the frame size has to be normalizedaccordingly, by using the normalization factors formixed frame types in the previous section. Theautocorrelation with one frame lag denotes thedegree of correlation between consecutive frames.The larger the frame size autocorrelation is, the lessthe difference in frame size between consecutive



frames. The autocorrelation with � frames lag isstated as:

R.�/ D E[.s.n/� �/.s.nC �/� �/]�2

, .1/

where � is the mean size of the frame, s.i/denotes the size of the ith frame and �2 is thevariation measured from the real video data. LetA1 denote the autocorrelation with one frame lag,i.e. R(1), then the curve can be fitted by a negativeexponential function with parameter ˛, which isthe autocorrelation coefficient and is used as oneof the parameters in the AR(1) model.4,12 Thus,we have

A1 D E[s.n/� �/.s.nC 1/� �/]�2

³ e�˛. .2/

Therefore, 1�A1 can denote the percentage error inbandwidth estimation of two consecutive frames.Let Ci,t1 be the real size of current frame withframe type t1 and CiC1,t2 be the estimated size ofthe next frame with frame type t2. The proposedbasic bandwidth prediction method uses the mean

frame size and the estimation error to predict thesize of the next frame as

CiC1,t2 D st2,t1 ŁMax.�,Ci,t1 C/, .3/

and D Ci,t1 .1� A1/, .4/

where St2,t1 is the normalization factor for differentframe types. Thus, St2,t1 can be regarded as the ratiobetween the mean frame sizes of frame type t2 andframe type t1. This value equals 1 for the sameframe type. The reason we choose the maximumvalue of � and Ci,t1 C is that we use the moreaggressive approach.

Table 1 shows the experimental result of theproposed correlation based prediction methodapplied to two video sources described in Section 2.Note that the fixed bandwidth allocation schememeans the bandwidth is fixed by the summationof the mean and the standard deviation. Themethod proposed in reference 6 is also listed forcomparison. The projected autoregressive (PAR)model13,14 is used as the video traffic source foran exhaustive simulation. The generated video

(a) Bandwidth allocation Cell loss (%) Bandwidthapproaches over allocation (%)

Max. Mean �

Fixed scheme 17.07 0.94 2.76 13.38Standard deviationbased scheme6 11.11 0.02 0.23 12.46Correlation basedscheme .k D 1/ 14.29 0.067 0.54 9.78Correlation basedscheme .k D 2/ 8.108 0.001 0.06 16.785

(b) Bandwidth allocation Cell loss (%) Bandwidthapproaches over allocation (%)

Max. Mean �

Fixed scheme 93.6 3 11.71 116.86Standard deviationbased scheme 87.64 1.17 6.64 130.13Correlation basedscheme .k D 1/ 64.91 1.13 4.36 33.42Correlation basedscheme .k D 2/ 61.029 0.421 2.692 46.61

Table 1. Bandwidth allocation for single video source condition: (a) the scene ‘Black andWhite’—intraframe only; (b) the scene ‘Red’s Nightmare’—mixed intra/interframes



data is packetized into ATM cells. Then, cell lossprobability is defined in terms of the percentageof cells lost in a frame due to the insufficientbandwidth allocated for that frame. The bandwidthover allocation is measured by the ratio of theallocated bandwidth, which is unused to thebandwidth actually used by a frame.

The experimental results show that, for thescene ‘Black and White’, the scheme used inreference 6 has smaller cell loss probability thanour prediction scheme. The reason is that thisscene contains only intraframes and each frameis coded individually, therefore, the effect ofthe frame correlation based prediction method isnot obvious. However, the proposed correlation-based method has a smaller percentage of overallocated bandwidth. Our method applies theheuristics of correlation to predict the framesize and therefore is more suitable for frameswith higher correlation property. For the scene‘Red’s Nightmare’, which contains a mixture ofI, P, and B frames, the proposed method canthen achieve a better prediction performancethan the scheme proposed in reference 6. Theratios of peak frame size and average framesize are 1.33 (1711/1282) for the ‘Black andWhite’ and 9.3 (27813/2991.37) for the ‘Red’sNightmare’, respectively. Thus, the content of‘Red’s Nightmare’ is more burst than that of the‘Black and White’. In Table 1, the proposed methoddemonstrates a better performance in both the cellloss probability and the bandwidth over allocation.The correlation based prediction method considersthe correlation property so that the over-allocatedbandwidth is dramatically reduced.

Note that is the estimated size differencebetween consecutive frames. One can enlarge thisvalue to decrease the cell loss probability if thebandwidth is sufficient. Thus, Equation (4) can berewritten as

D k ŁCi,t1 .1� A1/, .5/

where k can be dynamically assigned accordingto the bandwidth utilization and the numberof active connections in the network. The cellloss probability decreases as k increases andthe over-allocated bandwidth may proportionallyincrease. As observed from Table 1(a), the cell losspercentage can be reduced to be much less thanthe standard deviation based method when k D 2,

however, the over-allocated bandwidth is about4% more than that of the standard deviation basedmethod. Since the value of k can be dynamicallyassigned as the network status changes, it is veryuseful for the allocation of multiplexed sourcesunder a fixed bandwidth as described in thefollowing section.

Bandwidth Allocation for aMultiplexer

A multiplexer connected with several MPEGvideo sources are used as an example to study theeffectiveness of the proposed bandwidth allocationscheme. Assume that the bandwidth of the multi-plexer is limited and is of DS3 rate at its outputlink. The multiplexer allocates the bandwidth toeach arrival port during an allocation cycle. It ispossible that the bandwidth may be left or may beinsufficient within an allocation cycle because theallocated bandwidth of each arrival is based on theprediction.

Firstly, the experimental results of the proposedbandwidth prediction scheme with fixed valueof k are listed in Table 2. The fixed allocationmethod allocates the total bandwidth to eachsource equally and no bandwidth is left. The‘unallocated bandwidth’ column in the table is forthe proposed bandwidth allocation method onlyand is calculated in average for every allocationcycle. As shown in Table 2, the proposed methoddemonstrates a better performance than the fixedmethod especially when the number of arrivalsis large. The reason is that the statistical gain isobvious when the traffic load is high. It is alsoobserved that to increase the value of k from 1to 5 the effect is getting less significant as thenumber of arrivals increases. The reason is that thebandwidth becomes insufficient when the numberof arrivals is large and increasing k may decreasethe cell loss probability of the first few sources ofthe multiplexer because the estimated differenceincreases, but this also causes more cell losses ofthe last few sources due to the lack of bandwidth.

The predicted bandwidth of each arrival isbased on the correlation-based prediction methodproposed above, however, the value of k may beadjusted according to the bandwidth utilizationand the cell loss probability during a predefinedtime interval t. The value of k shall be dynamically



(a) Number of Cell loss ratio(%) Unallocatedinput sources bandwidth (%)

Proposed scheme Fixed scheme

10 1.141 0.176 84.58620 1.143 1.418 69.17945 1.191 5.981 30.70760 2.621 9.524 9.01370 9.227 12.359 1.227

(b) Number of Cell loss ratio(%) Unallocatedinput sources bandwidth (%)

Proposed scheme Fixed scheme

10 0.041 0.176 84.22020 0.041 1.418 68.44645 0.137 5.981 29.09760 2.285 9.524 7.60270 9.965 12.359 0.887

Table 2. Bandwidth allocation for multiplexed video sources when k is fixed:(a) k D 1; (b) k D 5

adapted in a heuristic manner. If k is selected toosmall, the cell loss probability and unallocatedbandwidth increase due to the small ; and if kis selected too large, the cell loss probability mayalso increase because most bandwidth is allocatedto the first few arrivals of the multiplexer andthere is not sufficient bandwidth for the rest of thearrivals. The relationship between k and the lossratio can be illustrated as in Figure 3. Thus, if thevalue of k can be arranged in a proper manner, thenthe bandwidth can be allocated effectively and thecell loss ratio can be decreased.

Basically, from Figure 3, we can find someheuristic that is very helpful for the adjustmentof the value of k. Thus, if the loss ratio increases as

Loss Ratio

k

Figure 3. The relationship between k and the CellLoss Rate

the value of k increases, then it is suggested thatk decrease in the next allocation cycle; and if theloss ratio decreases as the value of k increases, thenthe value of k shall keep increasing. Let kT denotethe value of k used during the time interval (T,T C t), then

kTCt D kT C υ, .6/

and

υ D

Btotal

BusedŁ RT�t � RT

Max[RT�t ,RT ]kT > kT�t

Btotal

BusedŁ RT � RT�t

Max[RT�t ,RT ]otherwise

.7/

where Btotal and Bused are the total bandwidth ofthe multiplexer and the mean of the bandwidthallocated in an allocation cycle during (T, T C t),respectively. And Rx denotes the mean cell lossprobability measured during (x, xC t). We can findthat the value of υ may be positive or negativedepending on the relationship between the changeof the cell loss probability and the values of k. Forexample, if kT is greater than kT�t and RT is smallerthan RT�t, then the value of υ is positive and k canbe increased for a smaller loss probability in thefollowing time interval. It is a trade-off to select thetime interval t for the consideration of fast reactionof the adjustment of υ and the processing timerequired to determine t.



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Figure 5. Performance of the multiplexer with mixed video sources

Simulation results of the proposed correlationbased allocation method with variable k valueand the fixed allocation method are illustratedin Figures 4 and 5. In this example, the timeinterval t is assumed to be 1 second (thus, every

25 frames). The fixed allocation scheme allocatesthe bandwidth to each source equally and nobandwidth is reserved. The experimental resultsshow that the proposed scheme illustrates muchbetter performance than the fixed scheme. And the



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Figure 6. Cell loss probabilities of different video sources

unallocated bandwidth decreases as the number ofinput sources increases. In order to investigate theperformance of the proposed scheme for MPEGsources with different traffic statistics, the ‘Blackand White’ and the ‘Red’s Nightmare’ are mixedand shuffled as the arrival of the multiplexer. Forthe fixed allocation scheme, the total bandwidth isallocated to each source according to the ratio ofthe mean frame sizes of both experimental MPEGsources. The experimental results of the mixedtraffic type are depicted in Figure 5. Note that themixed traffic has lower cell loss probability thanthat of unified traffic type depicted in Figure 4. Thereason is that the mean frame size of Source 2 ismuch greater than that of Source 1 and, therefore,the traffic load of the multiplexer connected withunified Source 2 (Figure 4) is higher than themixed traffic type when the number of the inputsource is the same. In Figure 6, we observe the lossprobabilities of Source 1 and Source 2, respectively,for the multiplexer with mixed traffic. It shows thatthe cell loss probability has no significant differencefor video sources with different statistics.

ConclusionsATM networks can provide flexible bandwidth

allocation for VBR traffic so that the bandwidth

resource can be utilized effectively. However, itis very difficult to predict and to manage therequired bandwidth of the VBR traffic exactly,unless the traffic characteristics can be describedvery precisely in the traffic descriptor at theconnection establishment stage. In this paper, acorrelation-based bandwidth prediction scheme,which utilizes the heuristics of the correlationbetween consecutive frames, is investigated fordynamic bandwidth allocation of the precodedMPEG video sources. In addition, the proposedscheme can adaptively adjust the bandwidthestimation error based on the performance statusof the network in order to achieve a betterperformance. The adjustment of the value of kis very similar to the learning process of a backpropagation neural network (BPN), i.e. the changeof k will be determined by the information feedbackfrom the network. The proposed scheme is appliedto study the management performance of the singlevideo source, a multiplexer connected with unifiedtraffic and mixed video sources. The experimentalresults show that the performance of the proposedscheme is superior to both the fixed bandwidthscheme and the standard deviation based scheme,especially for the video scene containing mixedframe types. Further investigation may extendthe proposed scheme to consider the bandwidthmanagement of the layered MPEG video sources.15



Basically, the B frame is less important than I andP frames in a video scene and can be assignedwith lower priority by setting the cell loss priority(CLP) bit of the ATM cell header. Therefore, it ispossible to manage the bandwidth more effectivelyby using these characteristics.

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11. Gall DL. MPEG: a video compression standard formultimedia applications. Communications of the ACM1991; 34: 47–58.

12. Lucantoni DM, Neuts ME, Reibman AR. Methods forperformance evaluation of VBR video traffic models.IEEE/ACM Trans. on Networking 1994; 2(2): 176–180.

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A heuristic approach of bandwidth management for video sources in ATM networks