A gamma-based framework for modeling variable-rate MPEG video sources: the GOP GBAR model

Frey, Michael; Nguyen-Quang, Son

doi:10.1109/90.893868

Cited by 71 publications

(40 citation statements)

References 30 publications

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“…In [19], a Gamma-beta-auto-regressive (GBAR) model is introduced for H.261 videoconference traffic, where each video sequence has Gamma marginal distribution and geometric autocorrelation. In [20], a variation of the GBAR model is proposed that takes into account the group-of-pictures (GoP) cyclicity. In [21], wavelet modeling is used to model the distribution of I frames and a time-domain model for P/B frame sizes, for MPEG-4 and H.264 videos.…”

Section: Related Workmentioning

confidence: 99%

Video scene identification and classification for user-tailored QoE in GEO satellites

Koutsakis

Spanou²,

Lazaris

2017

Hum. Cent. Comput. Inf. Sci.

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Satellite networks offer an efficient alternative where no terrestrial networks are available, and can offer a cost effective means of transferring data. Numerous proposals have addressed the problem of resource allocation in geostationary (GEO) satellites, and have been evaluated via various performance metrics related to user Quality of Service (QoS). However, ensuring that on average the users’ QoS requirements are satisfied does not guarantee the satisfaction of their individual Quality of Experience (QoE) requirements, especially in the case of bursty video users. This paper proposes the use of video scene identification and classification for traffic modeling at the scene level in order to improve resource allocation and user QoE in GEO satellite networks. The proposed call admission control and medium access control framework makes decisions based on available bandwidth, long-term and short-term user satisfaction and the revenue that the provider may gain.

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Section: Related Workmentioning

confidence: 99%

Video scene identification and classification for user-tailored QoE in GEO satellites

Koutsakis

Spanou²,

Lazaris

2017

Hum. Cent. Comput. Inf. Sci.

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“…Frey and Nguen-Quang [3] and Sarkar et al [12] propose the gamma distribution for the frame size. As a discrete version of the gamma distribution, let us assume that the distribution of the frame size is negative binomial.…”

Section: Numerical Examplesmentioning

confidence: 99%

<title>Multiserver queue with semi-Markovian batch arrivals with application to the MPEG frame sequence</title>

Takagi

2002

SPIE Proceedings

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We consider a queueing system consisting of multiple identical servers and a common queue. The service time follows an exponential distribution and the arrival process is governed by a semi-Markov process (SMP). The motivation to study the queueing system with SMP arrivals lies in that it can model the auto-correlated traffic on the high speed network generated by a real time communication, for example, the MPEG-encoded VBR video. Our analysis is based on the theory of piecewise Markov process. We first derive the distributions of the queue size and the waiting time. The stability condition of the system is also discussed. When the sojourn time of SMP follows an exponential distribution all the unknown constants contained in the generating function of queue size can be determined through the zeros of the denominator for this generating function. Based on the result of the analysis, we propose a model to evaluate the waiting time of MPEG video traffic on an ATM network with multiple channels. Here, the SMP corresponds to the exact MPEG sequence of frames. Finally, a numerical example using a real video data is shown.

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“…The I-frame must initiate every GoP. In our study we have used a typical GoP structure for a 25 Hz frame rate in VoD services IBBPBBPBBPBB or (12,3,2), where 12 means is the total length of GoP, 3 means the number of P-frames within the GoP, and 2 means the number of B-frames between two successive P-frames. Thus, in our case the stream of the frames at the output of the codec consists of the sequence of GoPs.…”

Section: Mpeg Coding and Compressionmentioning

confidence: 99%

“…As has been estimated [3], in the near future one of the major parts of the multimedia network load will consists of traffic generated by the video applications. Video-onDemand (VoD) service is an example of such applications.…”

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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A gamma-based framework for modeling variable-rate MPEG video sources: the GOP GBAR model

Cited by 71 publications

References 30 publications

Video scene identification and classification for user-tailored QoE in GEO satellites

Video scene identification and classification for user-tailored QoE in GEO satellites

<title>Multiserver queue with semi-Markovian batch arrivals with application to the MPEG frame sequence</title>

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

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