The Markov-modulated fluid models are mathematical tools widely used in telecommunication networks modeling. These models represent the discrete entities of the network traffic as a continuous fluid whose rates depend on a Markov process. The study of the fluid models shows that they are governed by a linear differential system. Many techniques are used to solve these equations such as spectral analysis, Laplace transforms, orthogonal polynomials and recurrence relations. The purpose of this work is to study mathematically the resolution techniques and to compare them in term of computational complexity, accuracy and stability.

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The constant evolution of video standards and technologies and the important growth of the amount of video data in networks leads to a need to build new models for video sources. In this paper, we specify a Markovian fluid model based on GoPs (Group of Pictures) that creates a video source descriptor. This descriptor can be used to compute the loss rate observed when transmitting the video via the network. We show also how to build an artificial video traffic having the same statistical characteristics of the original source.

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