Skip to main content
SpringerLink
Log in

Modelling H.264/AVC VBR video traffic: comparison of a Markov and a self-similar source model

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

We compare the adequacy of two models for realistic video sources, namely the fractional Brownian motion (fBm) and Markov modulated fluid flow (Mmff) models. We use the effective bandwidth approach to get the probability that the buffer content exceeds a certain threshold. We use a formula in which the variance function, i.e., the variance of the traffic arriving in an interval of length τ, plays a central role and we model it as variance associated either with the Mmff or fBm model.

We measure the variance function for an artificial source we construct, using Variable Bit Rate (VBR) H.264/AVC (Advanced Video Codec) video traces of real movies. There is a good correspondence between the buffer threshold exceeding probability obtained via trace-based simulations and the one predicted theoretically, based on the measured variance function. These we take as benchmark results against which we check both models—fBm and Mmff.

First, we try to tune the model parameters such that their variance function matches the measured one over a large range of τ values, but this proves to be difficult. When matching the variance function over a short range of the τ of interest, we conclude that the Mmff model is better suited to model VBR video sources. We conduct an error sensitivity analysis with non-optimal model parameters and conclude that they do not influence considerably the buffer exceeding probability.

Most reliable results are achieved with the measured variance function; if by some reason it needs to be modelled, the Mmff is preferred over the fBm model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Seeling, P., Reisslein, M., & Kulapala, B. (2004). Network performance evaluation using frame size and quality traces of single-layer and two-layer video: a tutorial. IEEE Communications Surveys and Tutorials, 6(3), 58–78.

    Article  Google Scholar 

  2. Auwera, G., David, P., & Reisslein, M. (2007). Video traffic analysis of H. 264/AVC and extensions: single-layer statistics (Technical Report) Tempe, USA: Arizona State University. http://trace.eas.asu.edu/publications/index.html.

  3. Advanced video coding for generic audiovisual services (2003/2007). ITU-T recommendation H. 264 (Nov. 2007).

  4. Norros, I. (1994). A storage model with self-similar input. Queueing Systems, 16, 387–396.

    Article  Google Scholar 

  5. Garrett, M., & Willinger, W. (1994). Analysis, modeling and generation of self-similar VBR video traffic. In Proceedings of SIGCOMM’94 (pp. 269–280).

  6. Molnár, S., Vidács, A., & Nilsson, A. (1997). Bottlenecks on the way towards fractal characterization of network traffic: estimation and interpretation of the hurst parameter. In International conference of the performance and management of complex communication networks (PMCCN’97) (pp. 17–21).

  7. Bashforth, B., & Williamson, C. (1998). Statistical multiplexing of self-similar video streams: simulation study and performance results. In Proceedings of the sixth international symposium on modeling, analysis, and simulation of computer and telecommunication systems (MASCOTS’98) (pp. 119–126).

  8. Courcoubetis, C., Dimakis, A., & Stamoulis, G. (1999). Traffic equivalence and substitution in a multiplexer. In Proceedings of IEEE INFOCOM’99 (pp. 1239–1247).

  9. Gao, J., & Rubin, I. (2001). Multifractal analysis and modeling of long-range dependent traffic. Computer Communications, 24(14), 1400–1410.

    Article  Google Scholar 

  10. Ansari, N., Liu, H., & Shi, Y. (2002). On modeling MPEG video traffics. IEEE Transactions on Broadcasting, 48(4), 337–347.

    Article  Google Scholar 

  11. Kelly, F. (1996). Notes on effective bandwidths. In Stochastic networks: theory and applications (pp. 141–168). Oxford: Oxford University Press.

    Google Scholar 

  12. Hong, S., Park, R., & Lee, C. (2001). Hurst parameter estimation of long-range dependent VBR MPEG video traffic in ATM networks. Journal of Visual Communication and Image Representation, 12(1), 44–65.

    Article  Google Scholar 

  13. Ryu, B., & Elwalid, A. (1996). The importance of long-range dependence of VBR video traffic in ATM traffic engineering: myths and realities. In Proceedings of SIGCOMM’96 (pp. 3–14).

  14. Grossglauser, M., & Bolot, J.-C. (1999). On the relevance of long-range dependence in network traffic. IEEE/ACM Transactions on Networking, 7(5), 629–640.

    Article  Google Scholar 

  15. Rikli, N.-E. (2004). Modeling techniques for VBR video: feasibility and limitations. Performance Evaluation, 57(1), 57–68.

    Article  Google Scholar 

  16. Rose, O. (1999). A memory Markov chain model for VBR traffic with strong positive correlations. In Proceedings of 16th ITC (pp. 827–836).

  17. Kempken, S., & Luther, W. (2007). Modeling of H. 264 high definition video traffic using discrete-time semi-Markov processes. In Proceedings of 20th ITC (pp. 42–53).

  18. Information technology-generic coding of moving pictures and associated audio information: systems (2000/2005). ISO/IEC 13818-1:2000 standard and ITU-T recommendation H. 222.0(2000)/Cor.3(01/2005).

  19. Krunz, M., & Ramasamy, A. (2000). The correlation structure for a class of scene-based video models and its impact on the dimensioning of video buffers. IEEE Transactions on Multimedia, 2(1), 27–36.

    Article  Google Scholar 

  20. Video traces available at: http://trace.eas.asu.edu/tracemain.html.

  21. Choe, J., & Shroff, N. (1997). A new method to determine the queue length distribution at an ATM multiplexer. In Proceedings of the IEEE INFOCOM’97 (pp. 549–556).

  22. Proakis, J., & Manolakis, D. (1988). Introduction to digital signal processing. New York: Macmillan Co.

    Google Scholar 

  23. Avramova, Z., De Vleeschauwer, D., Laevens, K., Wittevrongel, S., & Bruneel, H. (2007). Multiplexing gain of VBR video flows avoiding the self-similarity assumption. In Proceedings of fourth Euro-FGI workshop on new trends in modelling, quantitative methods and measurements (pp. 35–39).

  24. Reich, E. (1958). On the integrodifferential equation of Takács I. Annals of Mathematical Statistics, 29(2), 563–570.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SMACS Research Group, TELIN Department, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Z. Avramova, S. Wittevrongel & H. Bruneel

  2. Alcatel-Lucent Bell, Copernicuslaan 50, 2018, Antwerp, Belgium

    D. De Vleeschauwer & K. Laevens

Authors
  1. Z. Avramova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. De Vleeschauwer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Laevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Wittevrongel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. H. Bruneel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Z. Avramova.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Avramova, Z., De Vleeschauwer, D., Laevens, K. et al. Modelling H.264/AVC VBR video traffic: comparison of a Markov and a self-similar source model. Telecommun Syst 39, 91–102 (2008). https://doi.org/10.1007/s11235-008-9114-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-008-9114-0

Keywords

Search

Navigation