Adaptive multi-view video streaming using side information over peer-to-peer networks

Abstract

Multi-view plus-depth-map (MVD) video streaming with autostereoscopic displays provides multi-user immersive media experiences. In this context, delivery of MVD representation to multiple clients remains a challenging problem because of the high-volume of data involved and the inherent limitations imposed by the delivery networks. To this end, this paper investigates the side information (SI) assisted adaptation algorithm using peer-to-peer (P2P) systems. P2P delivery systems for MVD video can maximize link utilization, preventing the transport of multiple video copies of the same packet for many users. However, the quality of experience (QoE) can be significantly degraded by dynamic variations caused by network congestions. To this end, our solution comprises the extraction of low-overhead metadata at the encoding server that is distributed through the P2P network as SI and used by P2P clients performing network adaptation. In the proposed adaptation strategy, pre-selected views are discarded at times of network congestion and reconstructed with an optimal reconstruction performance using the delivered SI and the delivered neighboring camera views. The experimental results show that the robustness of P2P multi-view streaming using the proposed adaptation scheme is significantly increased in the P2P network.