Modelling of MPEG-4 traffic at GoP level using autoregressive processes

Golaup, A.; Aghvami, A.H.

doi:10.1109/vetecf.2002.1040721

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…Another model based on the AR approach was developed by Golaup and Aghvami for MPEG4 traffic [9]. The model does not attempt to capture long-range dependence in the traffic and cannot match the pdf.…”

Section: Related Workmentioning

confidence: 99%

VBR video traffic modeling and synthetic data generation using GA‐optimized Volterra filters

Abdennour

2006

Int J Network Mgmt

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SUMMARYVariable bit rate (VBR) video traffic models, which accurately represent the traffic characteristics and statistical properties of real videos, can provide significant information about expected traffic behavior. This knowledge can be used in the development of effective control schemes and improved network quality of service. An interesting class of models based on the idea of generating a number of chi-square sequences, by passing a Gaussian autoregressive (AR) process through a simple nonlinearity, was recently introduced. The gamma process in this class of models is obtained by a linear combination of the chi-square sequences. This model is simple and allows for arbitrary selection of both the AR model and the shape parameter of the gamma probability density function. However, the AR filter order is chosen mainly on a trial-and-error basis. In addition, while the approach uses a linear combination of K chi-square sequences, it fixes (K − 2) coefficients and solves for only the remaining two because it has more equations than unknowns. Occasionally, the resulting solution is not feasible and additional trials for different solutions are required. It is therefore the objective of this paper to use genetic algorithms to provide a more systematic approach to find the various model parameters. The paper also presents a thorough statistical analysis of the generated synthetic data in order to assess its suitability for representing MPEG video traffic. A comparison with published results is carried out in terms of how close are the means, standard deviations, and the autocorrelation functions to those of the real data. A comparison of over 10 000 replications and a number of different video traces reveals that a significant improvement can be achieved in almost all measures and for almost all the movies tested.

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Section: Related Workmentioning

confidence: 99%

VBR video traffic modeling and synthetic data generation using GA‐optimized Volterra filters

Abdennour

2006

Int J Network Mgmt

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“…An MPEG-4 scene is composed by a number of audiovisual objects, which can be either static or time-varying in nature [15]. The MPEG-4 standard is particularly designed for video streaming over wireless networks [47][48][49].…”

Section: Mpeg-4 Traffic Modelingmentioning

confidence: 99%

Using traffic prediction and estimation of provider revenue for a joint GEO satellite MAC/CAC scheme

Koutsakis

2010

Wireless Netw

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Satellite networks present an attractive solution in providing worldwide access to the present and future generation multimedia communication services. However, the burstiness of multimedia traffic and the long propagation delays in Geostationary (GEO) satellite systems call for an efficient Medium Access Control (MAC) protocol and an equally efficient Call Admission Control (CAC) scheme, in order to provide acceptable Quality of Service (QoS) to multimedia users. This paper proposes a fair and dynamic CAC and MAC framework, named Fair Predictive Resource Reservation Access (FPRRA), which is based on accurate videoconference traffic prediction and makes decisions after taking into account the provider revenue. The framework's performance is evaluated in comparison to other efficient schemes from the literature.

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“…This also has to be considered in modeling the streaming traffic. Autoregressive models [21] produce traffic with an exponentially decaying auto-correlation function. The characteristics of the video traffic streaming also depend on the compression standard adopted.…”

Section: Streaming Traffic Modelingmentioning

confidence: 99%

“…• A variable bit rate source using the autoregressive model [21]. • A variable bit rate source using the Fractional ARIMA model (F-ARIMA) [22].…”

Section: Streaming Traffic Modelingmentioning

confidence: 99%

A multimedia traffic modeling framework for simulation-based performance evaluation studies

Golaup

Aghvami

2006

Computer Networks

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Modelling of MPEG-4 traffic at GoP level using autoregressive processes

Cited by 9 publications

References 5 publications

VBR video traffic modeling and synthetic data generation using GA‐optimized Volterra filters

VBR video traffic modeling and synthetic data generation using GA‐optimized Volterra filters

Using traffic prediction and estimation of provider revenue for a joint GEO satellite MAC/CAC scheme

A multimedia traffic modeling framework for simulation-based performance evaluation studies

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