Mobile Networks and Applications

, Volume 3, Issue 1, pp 33–47 | Cite as

Adaptive rate controlled, robust video communication over packet wireless networks

  • G.R. Rajugopal
  • R.H.M. Hafez


Video transmission over wireless packet networks is gaining importance due to the concept of universal personal communication. Further, it is considered an important step towards wireless multimedia. The challenge however is to achieve good video quality over mobile channels, where typically the channel conditions vary due to signal fading. Hence this paper investigates adaptive rate controlled video transmission for robust video communication under packet wireless environment. A combination of mobile and an ATM backbone network is assumed in this work. An error resilient design for the video coder, as proposed in Rajugopal et al. (1996) is employed here. This video coder comprises wavelet transform (WT), multi-resolution motion estimation (MRME) and a robust design for zero tree quantization. Two configurations, one employing MRME and the other using 1D-WT for temporal analysis, are considered for the video coder. Adaptive dynamic rate control is required to adapt the video communication to the channel conditions. It provides more channel protection when the channel is severe and improves the source rate and hence the performance when the conditions are favorable. An algorithm for dynamic rate control under varying channel conditions is proposed in this paper. It is evaluated under narrowband and broadband channel conditions. From the results, it is concluded that the dynamic rate control is very effective in optimizing the quality under varying mobile channel conditions. It was observed that the dynamic rate control provides at least an acceptable video quality under severe channel conditions and a good video quality when the channel conditions are favorable.


Channel Condition Video Coder Unequal Error Protection Adaptive Rate Picture Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • G.R. Rajugopal
    • 1
  • R.H.M. Hafez
    • 2
  1. 1.ShareWave, Inc.El Dorado HillsUSA
  2. 2.Systems and Computer Engineering DepartmentCarleton UniversityOttawaCanada

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