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Dynamic Bandwidth Allocation Based on Online Traffic Prediction for Real-Time MPEG-4 Video Streams


The distinct characteristics of variable bit rate (VBR) video traffic and its quality of service (QoS) constraints have posed a unique challenge on network resource allocation and management for future integrated networks. Dynamic bandwidth allocation attempts to adaptively allocate resources to capture the burstiness of VBR video traffic, and therefore could potentially increase network utilization substantially while still satisfying the desired QoS requirements. We focus on prediction-based dynamic bandwidth allocation. In this context, the multiresolution learning neural-network-based traffic predictor is rigorously examined. A well-known-heuristic based approach RED-VBR scheme is used as a baseline for performance evaluation. Simulations using real-world MPEG-4 VBR video traces are conducted, and a comprehensive performance metrics is presented. In addition, a new concept of renegotiation control is introduced and a novel renegotiation control algorithm based on binary exponential backoff (BEB) is proposed to efficiently reduce renegotiation frequency.


  1. 1.

    Garrett MW, Willinger W: Analysis, modeling and generation of self-similar VBR video traffic. Proceedings of the ACM SIGCOMM Conference on Communications Architectures, Protocols and Applications, August–September 1994, London, UK 269–280.

    Google Scholar 

  2. 2.

    Rathgeb EP: Modeling and performance comparison of policing mechanisms for ATM networks. IEEE Journal on Selected Areas in Communications 1991,9(3):325–334. 10.1109/49.76630

    Article  Google Scholar 

  3. 3.

    Zhang H, Knightly EW: RED-VBR: a renegotiation-based approach to support delay-sensitive VBR video. Multimedia Systems 1997,5(3):164–176. 10.1007/s005300050053

    Article  Google Scholar 

  4. 4.

    Chong S, Li S, Ghosh J: Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM. IEEE Journal on Selected Areas in Communications 1995,13(1):12–23. 10.1109/49.363150

    Article  Google Scholar 

  5. 5.

    Wu M, Joyce RA, Wong H-S, Guan L, Kung S-Y: Dynamic resource allocation via video content and short-term traffic statistics. IEEE Transactions on Multimedia 2001,3(2):186–199. 10.1109/6046.923818

    Article  Google Scholar 

  6. 6.

    Liang Y: Real-time VBR video traffic prediction for dynamic bandwidth allocation. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews 2004,34(1):32-47. (special issue on technologies that promote computational intelligence, openness and programmability in networks and Internet services—part III) 10.1109/TSMCC.2003.818492

    MathSciNet  Article  Google Scholar 

  7. 7.

    Le Boudec J-Y, Verscheure O: Optimal smoothing for guaranteed service. IEEE/ACM Transactions on Networking 2000,8(6):689–696. 10.1109/90.893866

    Article  Google Scholar 

  8. 8.

    Rexford J, Sen S, Dey J, et al.: Online smoothing of live, variable-bit-rate video. Proceedings of the IEEE International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV '97), May 1997, St. Louis, Mo, USA 235–243.

    Google Scholar 

  9. 9.

    Adas AM: Using adaptive linear prediction to support real-time VBR video under RCBR network service model. IEEE/ACM Transactions on Networking 1998,6(5):635–644. 10.1109/90.731200

    Article  Google Scholar 

  10. 10.

    Chiruvolu G, Sankar R, Ranganathan N: VBR video traffic management using a predictor-based architecture. Computer Communications 2000,23(1):62–70. 10.1016/S0140-3664(99)00137-1

    Article  Google Scholar 

  11. 11.

    Yoo S-J, Kwak K-S, Kim M: Predictive and measurement-based dynamic resource management and QoS control for videos. Computer Communications 2003,26(14):1651–1661. 10.1016/S0140-3664(03)00003-3

    Article  Google Scholar 

  12. 12.

    Bocheck P, Chang S-F: Content-based VBR traffic modeling and its application to dynamic network resource allocation. In Res. Rep. 48c-98-20. Columbia University, New York, NY, USA; 1998.

    Google Scholar 

  13. 13.

    Bhattacharya A, Parlos AG, Atiya AF: Prediction of MPEG-coded video source traffic using recurrent neural networks. IEEE Transactions on Signal Processing 2003,51(8):2177–2190. 10.1109/TSP.2003.814470

    Article  Google Scholar 

  14. 14.

    Knightly EW, Zhang H: D-BIND: an accurate traffic model for providing QoS guarantees to VBR traffic. IEEE/ACM Transactions on Networking 1997,5(2):219–231. 10.1109/90.588085

    Article  Google Scholar 

  15. 15.

    Cruz RL: A calculus for network delay—I: network elements in isolation. IEEE Transactions on Information Theory 1991,37(1):114–131. 10.1109/18.61109

    MathSciNet  Article  Google Scholar 

  16. 16.

    Liang Y, Page EW: Multiresolution learning paradigm and signal prediction. IEEE Transactions on Signal Processing 1997,45(11):2858–2864. 10.1109/78.650113

    Article  Google Scholar 

  17. 17.

    Fitzek FHP, Reisslein M: MPEG-4 and H.263 video traces for network performance evaluation. IEEE Network 2001,15(6):40–54. (MPEG-4 traces, ) (MPEG-4 traces, ) 10.1109/65.967596

    Article  Google Scholar 

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Correspondence to Yao Liang.

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Liang, Y., Han, M. Dynamic Bandwidth Allocation Based on Online Traffic Prediction for Real-Time MPEG-4 Video Streams. EURASIP J. Adv. Signal Process. 2007, 087136 (2006).

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  • Video Traffic
  • Dynamic Bandwidth Allocation
  • Binary Exponential Backoff
  • Traffic Prediction
  • Video Trace