Multi-view wireless video streaming has the potential to enable a new generation of efficient and low-power pervasive surveillance systems that can capture scenes of interest from multiple perspectives, at higher resolution, and with lower energy consumption. However, state-of-the-art multiview coding architectures require relatively complex predictive encoders, thus resulting in high processing complexity and power requirements. To address these challenges, we consider a wireless video surveillance scenario and propose a new encoding and decoding architecture for multiview video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. It also uses estimates of image sparsity...