Multimedia Tools and Applications

, Volume 58, Issue 1, pp 125–146 | Cite as

A genetic approach to Markovian characterisation of H.264 scalable video



We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios.


H.264/SVC Traffic characterisation Markovian arrival process 



This work has been carried out in the framework of the Q-MATCH project sponsored by the Flemish Institute for the Promotion of Scientific and Technological Research in the Industry (IWT). The first author is a Postdoctoral Fellow with the Research Foundation, Flanders (F.W.O.-Vlaanderen), Belgium. A preliminary version of this paper was presented at the 15th International Conference on Analytical and Stochastic Modelling Techniques and Applications, Nicosia, Cyprus, 3–6 June 2008.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.SMACS Research Group, Department of Telecommunications and Information ProcessingGhent UniversityGentBelgium

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