A New Modeling Approach of H.263+ VBR Coded Video Sources in ATM Networks

Sisodia, G.; Guan, Ling; Hedley, Mark; De, S.

doi:10.1006/rtim.1999.0188

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…Relevant newer studies of videoconference traffic modelling reinforce the general conclusions obtained by the above earlier studies by evaluating and extending the existing models and also proposing new methods for successful and accurate modelling [14][15][16][17][18]. An extensive public available library of frame size traces of unconstrained and constrained MPEG-4, H.263, and H.263+ offline encoded video was presented in [19] along with a detailed statistical analysis of the generated traces.…”

Section: Introductionmentioning

confidence: 78%

“…e target video bit rates that were imposed in Case 2 are shown in Table 1. In each case, the UDP packets were captured by a network sniffer and the collected data were further postprocessed at the frame level (it is important to note here, that analysis at the MacroBlock (MB), as in [14], level has been examined and found to provide only a typical smoothing in the sample data. We believe that the analysis at the frame level is simpler and offers a realistic view of the traffic.)…”

Section: The Experimental and Measurement Workmentioning

confidence: 99%

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Characteristics of Video Traffic from Videoconference Applications: From H.261 to H.264

Domoxoudis

Kouremenos

Loumos

et al. 2013

Journal of Computer Networks and Communications

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is paper presents modelling results for H.26x video traffic generated by popular videoconference soware applications. e analysis of videoconference data, that were measured during realistic point-point videoconference sessions, led us to the general conclusion that the traffic can be distinguished into two categories: unconstrained and constrained. In the unconstrained traffic, there is a direct relation between the encoder and the form of the frequency histogram of the frame-size sequence. Moreover, for this type of traffic, strong correlations between successive video frames can be found. On the other hand, where bandwidth constraints are imposed during the encoding process, the generated traffic appears to exhibit similar characteristics for all the examined encoders with the very low autocorrelation values being the most notable one. On the basis of these results, this study proposes methods to calculate the parameters of a widely adopted autoregressive model for both types of traffic.

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Section: Introductionmentioning

confidence: 78%

Section: The Experimental and Measurement Workmentioning

confidence: 99%

Characteristics of Video Traffic from Videoconference Applications: From H.261 to H.264

Domoxoudis

Kouremenos

Loumos

et al. 2013

Journal of Computer Networks and Communications

View full text Add to dashboard Cite

show abstract

“…The target video bit rates that were imposed in Case 2 are shown in Table 1. In each case, the UDP packets were captured by a network sniffer and the collected data were further post-processed at the frame level (It is important to note here, that analysis at the MacroBlock (MB), as in [18], level has been examined and found to provide only a typical smoothing in the sample data. We believe that the analysis at the frame level is simpler and offers a realistic view of the traffic.)…”

Section: The Experimental and Measurement Workmentioning

confidence: 99%

An Introduction to Videoconference Video Traffic

Domoxoudis

Kouremenos

Loumos

et al. 2010

Organizational, Business, and Technological Aspects of the Knowledge Society

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Abstract. This paper presents modeling results for H.26x video traffic generated by popular videoconference software applications. The analysis of videoconference data that were measured during realistic point-point videoconference sessions, led us to the general conclusion that the traffic can be distinguished into two categories: unconstrained and constrained. In the unconstrained traffic, there is a direct relation between the encoder and the form of the frequency histogram of the frame-size sequence. Moreover, for this type of traffic, strong correlations between successive video frames can be found. On the other hand, where bandwidth constraints are imposed during the encoding process, the generated traffic appears to exhibit similar characteristics for all the examined encoders with the very low autocorrelation values being the most notable one.

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“…Specific parameters shown in Table 1a-1b, for the High and Low traces, depend on the particular coding 2 It is important to note, here, that analysis at the MacroBlock (MB), as in [16], level has been examined and found to provide only a typical smoothing in the sample data. We believe that the analysis at the frame level is simpler and offers a realistic view of the traffic.…”

Section: The Experimental and Measurement Workmentioning

confidence: 99%