An accurate and treatable Markov model of MPEG-video traffic

Lombardo, Alfio; Morabito, G.; Schembra, Giovanni

doi:10.1109/infcom.1998.659657

Cited by 57 publications

(37 citation statements)

References 12 publications

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“…These traces are well known and were used in several valuable studies [2,14,15]. Traces represent a sequence of the frame sizes where the size of each frame is presented in bits.…”

Section: Mpeg Coding and Compressionmentioning

confidence: 99%

“…During the last several years the main attention has again been paid to videotraffic modeling using discrete-and continuous-time Markov chains [6,14]. In contrast to the abovementioned Markov models, they involve an inverse-eigenvalue problem in order to derive the transition probability matrices.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the abovementioned Markov models, they involve an inverse-eigenvalue problem in order to derive the transition probability matrices. Such models produce an excellent approximation of both histogram of frame sizes and autocorrelation function of the empirical data but they are computationally inefficient because of the presence of t he inverse eigenvalue problem [14,15]. This drawback restricts their use in simulation studies where the need to produce frame sizes "on the fly" exists.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Two-Step MPEG Traffic Modeling Algorithm Based on a GBAR Process

Koucheryavy

Moltchanov

Harju

2003

IFIP Advances in Information and Communication Technology

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Abstract:In this paper we propose a two-step MPEG video sources modeling algorithm based on three auto-and cross-correlated gamma-beta autoregressive processes of order one GBAR(1). This algorithm is an extension of models originally proposed by Heyman [1] and Lombardo et al. [2]. We extend the model proposed by Heyman to the case of periodic frame sequence at the output of the codec. In order to do that we use information about the correlation between frame sizes within the group of pictures. Based on this information we introduce three cross-correlated GBAR(1) processes each of which models a particular type of the frame. The proposed algorithm captures the basic statistical characteristics of the empirical data well: the distribution of frame sizes and the autocorrelation function. Our algorithm is fairly simple computationally efficient and therefore, can be used in simulation studies.

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“…These traces are well known and were used in several valuable studies [2,14,15]. Traces represent a sequence of the frame sizes where the size of each frame is presented in bits.…”

Section: Mpeg Coding and Compressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Two-Step MPEG Traffic Modeling Algorithm Based on a GBAR Process

Koucheryavy

Moltchanov

Harju

2003

IFIP Advances in Information and Communication Technology

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“…the survey by Izquierdo and Reeves [6] on statistical traffic models for MPEG-1 encoded video. Proposed statistical models include Markovian processes [7][8][9][10][11][12], autoregressive processes [13][14][15][16], spatial renewal processes [17] and self-similar processes [18,19]. From the vantage point of queueing theory, Markovian processes are the most tractable: the associated queueing behaviour can be investigated efficiently by means of matrix analytic methods; see for example [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…A state is then an abstract concept of the Markov process which is deemed suitable for a trace if it accurately captures a number of statistics of this trace. Lombardo et al [8] construct a periodic Markov chain to account for the periodicity which is induced by the GoP structure of MPEG traces. A genetic algorithm is used to solve the inverse eigenvalue problem which relates the approximation of the autocorrelation function of the trace to the state transition probabilities of the Markov process.…”

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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A graphical tool for modeling correlated video traffic

Sousa-Vieira

2018

Comp Applic In Engineering

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Improvements in network infrastructures, storage capacity and computing power, along with advances in video coding technology, are enabling an increasing popularity of multimedia applications. Traffic modeling plays an important role in the performance evaluation of network architectures and protocols. In this paper, we describe a tool to deal with the process of modeling video traffic with persistent correlations in an easy and intuitive way: analysis of empirical traces, selection of models for the correlation and the marginal distribution, generation of synthetic traces and validation. A graphical user interface is presented to facilitate the process. The correlation models supported by the tool are based on the M/G/∞ process and the selection of parameters is carried out with the Whittle estimator. This tool can be used for modeling and simulation practices in engineering education.

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An accurate and treatable Markov model of MPEG-video traffic

Cited by 57 publications

References 12 publications

A Novel Two-Step MPEG Traffic Modeling Algorithm Based on a GBAR Process

A Novel Two-Step MPEG Traffic Modeling Algorithm Based on a GBAR Process

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

A graphical tool for modeling correlated video traffic

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