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Performance models of statistical multiplexing in packet video communications

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Maglaris, B., Anastassiou, D., Sen, P., Karlsson, G., Robbins, J.D.·1988·IEEE Transactions on Communications

Abstract

Models and results are presented that assess the performance of statistical multiplexing of independent video sources. Presented results indicate that the probability of buffering (or delaying) video data beyond an acceptable limit drops dramatically as the number of multiplexed sources increases beyond one. This demonstrates that statistical or asynchronous time-division multiplexing (TDM) can efficiently absorb temporal variations of the bit rate of individual sources without the significant variations in reception quality exhibited by multimode videocoders for synchronous TDM or circuit-switched transmission. Two source models are presented. The first model is an autoregressive continuous-state, discrete-time Markov process, which was used to generate source data in simulation experiments. The second model is a discrete-state, continuous-time Markov process that was used in deriving a fluid-flow queuing analysis. The presented study shows that both models generated consistent numerical results in terms of queuing performance.>

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Metadata

Authors
Maglaris, B., Anastassiou, D., Sen, P., Karlsson, G., & Robbins, J. D.
Venue
IEEE Transactions on Communications
Published
1988-07
Volume
36
Issue
7
Pages
834-844
ISSN
0090-6778 (ISSN-L)
OpenAlex
W2158883695
Cited by
982
References
0
FWCI
30.49
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Performance models of statistical multiplexing in packet video communications. https://app.cochat.ai/papers/10.1109/26.2812
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Maglaris, B., Anastassiou, D., Sen, P., Karlsson, G., & Robbins, J. D. (1988). Performance models of statistical multiplexing in packet video communications. IEEE Transactions on Communications. https://doi.org/10.1109/26.2812

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.