Energy-aware video coding of multiple views via workload balancing

Video coding and compression reduce the storage space and/or the bandwidth required to transmit video. Multiview video systems make use of more than one camera to describe a scene and therefore demand more computing resources and energy consumption to compress and transmit video data. In this paper, we present an energy management framework for multiview systems that combines the two major video coding and compression paradigms: Predictive and Distributed Video Coding (PVC and DVC). The paradigms generally assume that the decoder (in PVC) and the encoder (in DVC) are resource constrained devices. However, there are a growing number of applications where resources are constrained at both encoder and decoder. Such scenarios cannot be handled due to the imbalance in PVC/DVC and demand a more flexible paradigm. In the proposed framework, video coding that combines PVC and DVC is used to balance multiview video coding workload between the video encoder and decoder in a way that maximizes system lifetime. Simulation results show that the proposed method can obtain a lifetime percentage increase of 58% and 30% on average and maintain video quality when compared to strict PVC and DVC systems respectively.