Optimization for image transmission over varying channel with MCMC
Existing work in media transmission generally assumes that the channel condition is stationary. However, communication channels are often varying with time in practice. Adaptive design needs frequent feedback for channel updates, which is often impractical due to the complexity and delay. In this article, we design the unequal error protection for image transmission over noisy varying channels based on their distribution functions. Since the channel effect must be marginalized in order to find the appropriate rate allocation, the optimization problem is very complex. We propose to solve this problem using the Markov Chain Monte Carlo (MCMC) method. The cost function is first mapped into a multi-variable probability distribution. Then, with the detailed balance, MCMC is designed to generate samples from the mapped stationary probability distribution so that the optimal solution is the one that gives the lowest data distortion. We also show that the rate allocation design considering the channel probability function works better than the design considering the mean value of the channel.
markov chain monte carlo progressive image transmission optimal rate-allocation