On Modeling Video Traffic from Multiplexed MPEG-4 Videoconference Streams

  • A. Lazaris
  • P. Koutsakis
  • M. Paterakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4003)


Due to the burstiness of video traffic, video modeling is very important in order to evaluate the performance of future wired and wireless networks. In this paper, we investigate the possibility of modeling this type of traffic with well-known distributions. Our results regarding the behavior of single videoconference traces provide significant insight and help to build a Discrete Autoregressive (DAR(1)) model to capture the behavior of multiplexed MPEG-4 videoconference movies from VBR coders.


Video Frame Long Range Depend Video Packet Video Traffic Markov Renewal Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lucantoni, D.M., Neuts, M.F., Reibman, A.R.: Methods for Performance Evaluation of VBR Video Traffic Models. IEEE/ACM Trans. Networking 2, 176–180 (1994)CrossRefGoogle Scholar
  2. 2.
    Nomura, M., Fuji, T., Ohta, N.: Basic Characteristics of Variable Rate Video Coding in ATM Environment. IEEE Journal on Selected Areas in Communications 7(5), 752–760 (1989)CrossRefGoogle Scholar
  3. 3.
    Heyman, D.P., Tabatabai, A., Lakshman, T.V.: Statistical Analysis and Simulation Study of Video Teleconference Traffic in ATM Networks. IEEE Transactions on Circuits and Systems for Video Technology 2(1), 49–59 (1992)CrossRefGoogle Scholar
  4. 4.
    Dawood, A.M., Ghanbari, M.: Content-based MPEG Video Traffic Modeling. IEEE Transactions on Multimedia 1(1), 77–87 (1999)CrossRefGoogle Scholar
  5. 5.
    Melamed, B., Pendarakis, D.E.: Modeling Full-Length VBR Video Using Markov-Renewal Modulated TES Models. IEEE Journal on Selected Areas in Communications 16(5), 600–611 (1998)CrossRefGoogle Scholar
  6. 6.
    Chandra, K., Reibman, A.R.: Modeling One- and Two-Layer Variable Bit Rate Video. IEEE/ACM Transactions on Networking 7(3), 398–413 (1999)CrossRefGoogle Scholar
  7. 7.
    Ren, Q., Kobayashi, H.: Diffusion Approximation Modeling for Markov Modulated Bursty Traffic and its Applications to Bandwidth Allocation in ATM Networks. IEEE Journal on Selected Areas in Communications 16(5), 679–691 (1998)CrossRefGoogle Scholar
  8. 8.
    Heyman, D.P.: The GBAR Source Model for VBR Videoconferences. IEEE/ACM Transactions on Networking 5(4), 554–560 (1997)CrossRefGoogle Scholar
  9. 9.
    Frey, M., Ngyuyen-Quang, S.: A Gamma-Based Framework for Modeling Variable-Rate Video Sources: The GOP GBAR Model. IEEE/ACM Trans. on Networking 8(6), 710–719 (2000)CrossRefGoogle Scholar
  10. 10.
    Xu, S., Huang, Z.: A Gamma autoregressive video model on ATM networks. IEEE Transactions on Circuits and Systems Video Technology 8(2), 138–142 (1998)CrossRefGoogle Scholar
  11. 11.
    Krunz, M., Tripathi, S.K.: On the Characterization of VBR MPEG Streams. In: Proceedings of ACM SIGMETRICS, vol. 25 (June 1997)Google Scholar
  12. 12.
    Sarkar, U.K., Ramakrishnan, S., Sarkar, D.: Modeling Full-Length Video Using Markov-Modulated Gamma-Based Framework. IEEE/ACM Trans. on Networking 11(4), 638–649 (2003)CrossRefGoogle Scholar
  13. 13.
    Rose, O.: Statistical Properties of MPEG Video Traffic and Their Impact on Traffic Modeling in ATM Systems. In: Proceedings of the 20th Annual Conference on Local Computer Networks (October 1995)Google Scholar
  14. 14.
    Arifler, D., Evans, B.L.: Modeling the Self-Similar Behavior of Packetized MPEG-4 Video Using Wavelet-Based Methods. In: Proceedings of the IEEE International Conference on Image Processing, Rochester, New York, USA, pp. 848–851 (2002)Google Scholar
  15. 15.
    Fitzek, F.H.P., Reisslein, M.: MPEG-4 and H.263 Video Traces for Network Performance Evaluation. IEEE Network 15(6), 40–54 (2001)CrossRefGoogle Scholar
  16. 16.
    Heyman, D.P., Lakshman, T.V.: What are the Implications of Long-Range Dependence for VBR-Video Traffic Engineering. IEEE/ACM Transactions on Networking 4(3), 301–317 (1996)CrossRefGoogle Scholar
  17. 17.
    Ryu, B.K., Elwalid, A.: The Importance of Long-Range Dependence of VBR Video Traffic in ATM Traffic Engineering: Myths and Realities. In: Proceedings of the ACM SIGCOMM 1996, Stanford, CA, USA, pp. 3–14 (1996)Google Scholar
  18. 18.
    Beran, J., Sherman, R., Taqqu, M.S., Willinger, W.: Long-Range Dependence in Variable Bit-Rate Video Traffic. IEEE Transactions on Communications 43(2/3/4), 1566–1579 (1995)CrossRefGoogle Scholar
  19. 19.
    Krunz, M., Hughes, H.: A Traffic Model for MPEG-coded VBR Streams. In: Proceedings of the ACM SIGMETRICS, Ottawa, Canada, pp. 47–55 (1995)Google Scholar
  20. 20.
    Law, A.M., Kelton, W.D.: Simulation Modeling & Analysis, 2nd edn. McGraw Hill Inc., New York (1991)zbMATHGoogle Scholar
  21. 21.
    Jacobs, P.A., Lewis, P.A.W.: Time Series Generated by Mixtures. Journal of Time Series Analysis 4(1), 19–36 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Lakshman, T.V., Ortega, A., Reibman, A.R.: VBR Video: Trade-offs and potentials. Proceedings of the IEEE 86(5), 952–973 (1998)CrossRefGoogle Scholar
  23. 23.
    Park, K., Willinger, W. (eds.): Self-Similar Network Traffic and Performance Evaluation. John Wiley & Sons, Inc., Chichester (2000)Google Scholar
  24. 24.
    Schafer, R.: MPEG-4: A Multimedia Compression Standard for Interactive Applications and Services. IEE Electronics and Communications Engineering Journal 10(6), 253–262 (1998)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Lazaris
    • 1
  • P. Koutsakis
    • 1
  • M. Paterakis
    • 1
  1. 1.Dept. of Electronic and Computer Engineering, Information & Computer Networks LaboratoryTechnical University of CreteChaniaGreece

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