Joint optimization of quantization and on-line channel estimation for low bit-rate video transmission

Optimal quantization and channel estimation are one among the main issues in low bit-rate video transmission over time varying noisy channels. Previous approaches to these issues were mainly based on quantizers optimized for parametric channel models and pilot symbol aided techniques for channel identification. However, this optimality may not hold when the randomly varying channel behavior deviates from these models. Also, the cost involved in terms of delay could be large for pilot symbol based channel estimation. We propose a new empirically optimized channel matched quantizer and a stochastic learning algorithm that estimates and tracks the channel with minimal additional delay and overhead. Performance analysis of the algorithm shows that the new adaptive quantizer results in a better quality video. The learning algorithm converges very fast