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Bayesian traffic dynamics and packet loss prediction for video over IP networks

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Abstract

Designing good network-adaptive, error resilient video coders for IP networks is a challenging task. Video data packets can be lost as a consequence of congestion in the network, causing a degradation in video quality at the receiver side. Predicting the packet loss probability is therefore an important step in the design of an efficient network-adaptive video coder.

In this paper, we present a novel Bayesian-based approach that can dynamically predict the instantaneous packet loss probability as well as estimate the probability distribution of the cross-traffic rate. The method utilises observed losses and end-to-end measurements of the packet delays to predict the time-varying cross-traffic rate in the network. The posterior probability for the cross-traffic rate is established by using the Bayes formulation. To obtain this, the probability of delays conditioned on the cross-traffic are derived by decomposing the delays with a multi-scale wavelet transform. The probability of the observed losses conditioned on the cross-traffic are derived by studying the queueing properties in the network. The predicted packet loss probability is then derived from the posteriori distribution of the cross-traffic rate and the expected queueing behaviour. An algorithm for obtaining the numerical solution is also given.

Two sets of simulations are performed to demonstrate the effectiveness of the proposed method. The first set is designed to test the performance of the proposed Bayesian method for dynamically predicting the packet loss probabilities. The results of the predicted packet loss probabilities are then compared with the ground truth of packet loss values for three different network traffic generators. In the 2nd set of simulations, the proposed method and the two-state Markov model are incorporated separately into a network-adaptive video codec whose coding rate is adapted to the predicted packet loss probabilities. The reconstructed videos using these two methods are then compared.

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Authors and Affiliations

  1. Department of Signals and Systems, Chalmers University of Technology, 41296, Göteborg, Sweden

    Andrew Backhouse & Irene Y. H. Gu

Authors
  1. Andrew Backhouse
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  2. Irene Y. H. Gu
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Correspondence to Andrew Backhouse.

Additional information

Andrew Backhouse received M.Math. and M.Sc. degree in Mathematics from the University of York (UK) in 2001 and 2002, respectively. He is currently a Ph.D. student at Chalmers University of Technology, Gothenburg, Swedem. His research interests include multiple description coding, frame theory, Internet network probing and packet loss prediction.

Irene Yu-Hua Gu received the Ph.D. degree in electrical engineering from Eindhoven University of Technology, The Netherlands, in 1992. She is Professor in the Department of Signals and Systems at Chalmers University of Technology, Sweden. She was a research fellow at Philips Research Institute IPO (NL) and Staffordshire University (UK), and a lecturer at The University of Birmingham (UK) from 1992 to 1996. Since 1996 she has been with the Department of Signals and Systems at Chalmers University of Technology (Sweden). Her current research interests include image processing, video surveillance and object tracking, video communications, and signal processing applications to electric power systems.

Dr. Gu has served as an Associate Editor for the IEEE Transactions on Systems, Man and Cybernetics from 2000 to 2005, the Chair-Elect of the IEEE Swedish Signal Processing Chapter (Sweden) from 2002 to 2004, and is a Member of the Editorial Board for the EURASIP journal of Applied Signal Processing since July 2005.

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Backhouse, A., Gu, I.Y.H. Bayesian traffic dynamics and packet loss prediction for video over IP networks. Multimedia Systems 11, 468–479 (2006). https://doi.org/10.1007/s00530-006-0016-2

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