Modeling of H.264 High Definition Video Traffic Using Discrete-Time Semi-Markov Processes

Kempken, Sebastian; Luther, Wolfram

doi:10.1007/978-3-540-72990-7_8

Cited by 12 publications

(15 citation statements)

References 14 publications

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“…Moltchanov et al [10] avoid this inverse eigenvalue problem by considering Markov chains with additional structure. Finally, Kempken and Luther [22] consider single-layer H.264 video at the GoP level and use a genetic algorithm to assign states to the GoPs such that the statistics of the model accurately approximate those of the trace. These authors show that a genetic approach yields Markovian models with small state spaces that can capture the time correlation of the trace accurately.…”

Section: Introductionmentioning

confidence: 99%

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

Fiems

Steyaert

Bruneel

2011

Multimed Tools Appl

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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.

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Section: Introductionmentioning

confidence: 99%

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

Fiems

Steyaert

Bruneel

2011

Multimed Tools Appl

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“…It is easily shown that the mean vector and the covariance matrix of the DBMAP with parameters as given in equation (14) match the sample mean and sample covariance matrix of the trace. To capture temporal correlation, the authors of [15] focus on matching the autocorrelation function of the DBMAP with the autocorrelation function of the trace. However, the autocorrelation of a DBMAP only depends on the covariance matrix Ω of the DBMAP and not on the covariance matrices Ω i in the different states of the DBMAP.…”

Section: Genetic Characterisationmentioning

confidence: 99%

“…The genetic approach for video characterisation has recently been proposed by Kempken and Luther [15] in the context of characterising single layer H.264 video sources at the group of pictures (GOP) level. These authors show that a genetic approach yields Markovian models with small state spaces that can capture the time correlation of the trace accurately.…”

Section: Introductionmentioning

confidence: 99%

“…These authors show that a genetic approach yields Markovian models with small state spaces that can capture the time correlation of the trace accurately. We here extend the approach of [15] to Markovian characterisation of multi-layered video at the sub-GOP level. In particular, the different types of slices are grouped into a number of traffic classes and the H.264/SVC source is investigated at the traffic class level.…”

Section: Introductionmentioning

confidence: 99%

“…A key notion of the present approach is the matrix-valued covariance function which is used to assess the fitness (see further) of the Markov model. As opposed to the correlation function used in [15], the covariance function depends on the second order moments in each of the Markov states. Hence, inter-layer correlation is taken into account while assessing the fitness of the model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Markovian Characterisation of H.264/SVC Scalable Video

Fiems

Inghelbrecht

Steyaert

et al.

Analytical and Stochastic Modeling Techniques and Applications

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Abstract. In this paper, a multivariate Markovian traffic model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios.

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Using the Whittle estimator for VBR video traffic model selection in the spectral domain

Sousa-Vieira

2013

Trans. Emerging Tel. Tech.

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Improvements in network infrastructures along with advances in video coding technology are enabling an increasing popularity of multimedia applications. Consequently, video traffic represents an important part of the load in modern networks, and adequate models play an important role in the performance evaluation of network architectures and protocols. This paper presents a method that uses the Whittle estimator to choose, between several models for variable-bit-rate video traffic based on the M/G/1 process, the one that gives rise to a better adjustment of the spectral density, and therefore of the correlation structure, of the traffic to model.

show abstract

Modeling of H.264 High Definition Video Traffic Using Discrete-Time Semi-Markov Processes

Cited by 12 publications

References 14 publications

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

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

Markovian Characterisation of H.264/SVC Scalable Video

Using the Whittle estimator for VBR video traffic model selection in the spectral domain

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