A power-efficient cloud-based compressive sensing video communication system

Mobile devices performing video coding and streaming over wireless communication networks are limited in energy supply. In this paper, a power-efficient fast cloud-based Compressive Sensing (CS) video communication system framework is proposed, which shifts the heavy computational burden from mobile devices to cloud thus the operational lifetime of the mobile devices can be prolonged. Firstly, a CS-based video encoder with partial-bidirectional motion interframe prediction is proposed to greatly reduce the complexity of video encoding, and then a BER-based adaptive CSEC scheme is proposed to combat the data loss in wireless networks. Meanwhile, cloud platform is employed, which has strong ability of computation and processing to solve the problem of high complexity resulted from CS reconstruction. Simulation results show that the energy consumption of the proposed system can be substantially reduced. Under the condition of the same reconstructed video quality (SSIM=0.9), the proposed scheme only takes about 15% percent power consumption of the state-of-the-art H.264 encoding with conventional FEC-based transmitting scheme.