Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in complex real-world scenarios. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper. First, we propose a Cross-view Dictionary Learning (CDL) model, which is a general solution to the multi-view learning problem. Inspired by the dictionary learning based domain adaptation, CDL learns a pair of dictionaries from two views. In particular, CDL adopts a projective learning strategy, which is more efficient than the optimization in traditional dictionary learning. Second, we propose a Cross-view Multi-level Dictionary Learning (CMDL) approach based on CDL. CMDL contains dictionary learning models at different representation levels, including image-level, horizontal part-level, and patch-level. The proposed models take advantages of the view-consistency information, and adaptively learn pairs of dictionaries to generate robust and compact representations for pedestrian images. Third, we incorporate a discriminative regularization term to CMDL, and propose a CMDL-Dis approach which learns pairs of discriminative dictionaries in image-level and part-level. We devise efficient optimization algorithms to solve the proposed models. Finally, a fusion strategy is utilized to generate the similarity scores for test images. Experiments on the public VIPeR, CUHK Campus, iLIDS, GRID and PRID450S datasets show that our approach achieves the state-of-the-art performance.