The innovations in the field of sensor technology and the advent of Internet of Things entails increased application of Wireless Visual Sensor Networks (WVSNs) in various sensing and monitoring scenarios. A set of camera sensor nodes are deployed to cover an area of interest and these sensor nodes are capable to capture, process and transmit image to a sink node for further processing. Sensor nodes are usually battery powered and hence energy constrained, which demands an energy efficient data gathering and forwarding scheme to prolong the network lifetime. Compared to traditional Wireless Sensor Networks (WSNs), a huge volume of data is involved in WVSN and hence efficient data compression algorithms are required to reduce the transmission cost without increasing the complexity of the encoder. In this article, we present a video compression algorithm based on block compressive sensing (BCS) having significantly high compression ratio (CR) compared to state-of-the-art compressive sensing based schemes. A second level compression performed at the encoder and an iterative reconstruction based on multihypothesis prediction performed at the decoder resulted in a video compression algorithm with high CR and reconstruction quality without considerably increasing the encoder/decoder complexity, making it more suitable for WVSN.