Joint source-channel coding for scalable video

Lisimachos P. Kondi*, Faisal Ishtiaq, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


An approach towards joint source-channel coding for wireless video transmission at low bit rates is proposed. An SNR scalable video coder is used and a different amount of error protection is allowed for each scalable layer. Our problem is to allocate the available bit rate across scalable layers and, within each layer, between source and channel coding, while minimizing the end-to-end distortion of the received video sequence. The distortion is due to both source coding (quantization) errors and channel noise errors, and is measured in terms of the Mean Squared Error (MSE). The optimization algorithm utilizes rate-distortion characteristic plots. These plots show the contribution of each layer to the total distortion as a function of the source rate of the layer and the residual bit error rate (the error rate that remains after the use of channel coding). The plots are obtained experimentally using representative video sequences and show the sensitivity of the source encoder and decoder to channel errors. Our algorithm is operationally optimal given the rate-distortion characteristic plots. These plots are used in conjunction with plots that show the bit error rate achieved by the allowable channel coding schemes for given channel conditions in order to obtain the operational rate-distortion curve of each layer. Then, dependent Lagrangian optimization is used to determine the overall bit allocation across all layers.

Original languageEnglish (US)
Pages (from-to)324-335
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Jan 1 2000
EventImage and Video Communications and Processing 2000 - San Jose, CA, USA
Duration: Jan 25 2000Jan 28 2000

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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