Abstract : Distributed source coding is a technique that allows to compress several correlated sources, without any cooperation between the encoders, and without rate loss provided that the decoding is joint. Motivated by this principle, distributed video coding has emerged, exploiting the correlation between the consecutive video frames, tremendously simplifying the encoder, and leaving the task of exploiting the correlation to the decoder. The first part of our contributions in this thesis presents the asymmetric coding of binary sources that are not uniform. We analyze the coding of non-uniform Bernoulli sources, and that of hidden Markov sources. For both sources, we first show that exploiting the distribution at the decoder clearly increases the decoding capabilities of a given channel code. For the binary symmetric channel modeling the correlation between the sources, we propose a tool to estimate its parameter, thanks to an EM algorithm. We show that this tool allows to obtain fast estimation of the parameter, while having a precision that is close to the Cramer-Rao lower bound. In the second part, we develop some tools that facilitate the coding of the previous sources. This is done by the use of syndrome-based Turbo and LDPC codes, and the EM algorithm. This part also presents new tools that we have developed to achieve the bounds of asymmetric and non-asymmetric distributed source coding. We also show that, when it comes to non-uniform sources, the roles of the correlated sources are not symmetric. Finally, we show that the proposed source models are well suited for the video bit planes distributions, and we present results that proof the efficiency of the developed tools. The latter tools improve the rate-distortion performance of the video codec in an interesting amount, provided that the correlation channel is additive.