Super-resolution as an exciting application in image processing was studied widely in the literature. This paper presents new approaches to video super-resolution based on sparse coding and belief propagation. First, find candidate match pixels on multiple frames using sparse coding and belief propagation. Second, incorporate information from these candidate pixels with weights computed using the nonlocal-means method in the first approach or using the sparse coding and belief propagation method in the second approach. The effectiveness of the proposed methods is demonstrated for both synthetic and real video sequences in the experiment section. In addition, the experimental results show that our models are naturally robust in handling super-resolution on video sequences affected by scene motions and/or small camera motions.