||Handling the noise in source video signal and the error introduced during transmission are important for robust video communication. In this thesis, we investigate these two problems and propose reasonable solutions for each of them. Specifically, to remove the noise from the source video, we propose an efficient video denoising algorithm based on maximum a posteriori (MAP) estimation. We show that video denoising can be regarded as a rate distortion optimization problem under some appropriate assumptions. Therefore, denoising and encoding can be performed simultaneously, which makes the denoising process almost costless. Moreover, we describe in details how to select suitable coding parameters. On the other hand, to conceal the error introduced during transmission, we propose a novel two-stage error concealment scheme. In the first stage, we propose a novel spatio-temporal boundary matching algorithm (STBMA) to reconstruct the lost motion vectors (MV). A well defined cost function is introduced which exploits both spatial and temporal smoothness properties of video signals. In the second stage, we use a novel partial differential equation (PDE) based algorithm to refine the reconstruction. We minimize, in a weighted manner, the difference between the gradient field of the reconstructed MB in current frame and that of the reference MB in the reference frame under given boundary condition. A weighting factor is used to control the regulation level according to the local blockiness degree. In the experiments, we achieve better results than other stat-of-art methods for both video denoising and error concealment.