Adaptive 3D multi-view video streaming over P2P networks

Streaming 3D multi-view video to multiple clients simultaneously remains a highly challenging problem due to the high-volume of data involved and the inherent limitations imposed by the delivery networks. Delivery of multimedia streams over Peer-to-Peer (P2P) networks has gained great interest due to its ability to maximise link utilisation, preventing the transport of multiple copies of the same packet for many users. On the other hand, the quality of experience can still be significantly degraded by dynamic variations caused by congestions, unless content-aware precautionary mechanisms and adaptation methods are deployed. In this paper, a novel, adaptive multi-view video streaming over a P2P system is introduced which addresses the next generation high resolution multi-view users' experiences with autostereoscopic displays. The solution comprises the extraction of low-overhead supplementary metadata at the media encoding server that is distributed through the network and used by clients performing network adaptation. In the proposed concept, pre-selected views are discarded at a times of network congestion and reconstructed with high quality using the metadata and the neighbouring views. The experimental results show that the robustness of P2P multi-view streaming using the proposed adaptation scheme is significantly increased under congestion