Multi-View Wireless Video Streaming based on Compressed Sensing: Architecture and Network Optimization

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 multi-view 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 multi-view 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 to perform efficient rate adaptation and effectively exploit inter-view correlation at the encoder side.

Based on the proposed encoding/decoding architecture, we further develop a CS-based end-to-end rate distortion model by considering the effect of packet losses on the perceived video quality. We then introduce a modeling framework to design network optimization problems in a multi-hop wireless sensor network. Extensive performance evaluation results show that the proposed coding framework and power-minimizing delivery scheme are able to transmit multi-view streams with guaranteed video quality at low power consumption.