The development of multi-view video has paved the way for emerging 3D applications. In general multi-view video streaming, video frames for all viewpoints, i.e., cameras, must be transmitted to viewers because the view-switching demands of all viewers are unpredictable. However, existing transmission schemes are highly vulnerable to frame loss. Specifically, frame loss in one viewpoint can induce a collapse in decoding for other viewpoints. To improve loss-resilience, this paper proposes a multi-path based multi-view video transmission scheme. Our scheme encodes video frames into multiple versions that are independent of each other, using inter-view prediction. The scheme then transmits each version using multiple transmission paths. Our scheme makes three contributions: 1) it reduces video traffic even for a large number of cameras, 2) it prevents an increase in the number of undecoded video frames caused by single-frame loss, and 3) it conceals frame loss by taking video frames from other paths. Evaluations show that our proposed scheme improves video quality by 3 dB, as compared to existing transmission schemes in loss-prone environments.

In conventional multiview video systems using progressive download, a user downloads videos of all viewpoints of one content to realize smooth view switching. This, however, increases the video traffic, and if the available download rate is low, the video quality suffers. Downloading only the desired viewpoint is one approach for reducing the traffic. However, in this case, playback stalls will occur after view switching. These stalls degrade the user's satisfaction for the application. In this paper, we aim at two objectives: 1) to achieve reduction in video traffic and 2) to minimize the number of playback stalls. To this end, we propose a new multiview video delivery scheme for progressive download. The main idea of the proposed scheme is that the user downloads a part of viewpoints only, which will be played back by the user with a high probability, to realize both traffic reduction and smooth view switching. In addition, we propose two download-scheduling algorithms to prevent playback stalls even at low download rates. The first algorithm prevents stalls in the cases with frequent view switching, such as zapping, while the second prevents stalls in gazing cases. Evaluations using a Joint Multiview Video Coding (JMVC) encoder and multiview video sequences show that our scheme achieves not only reduced video traffic but also decreased number of playback stalls, regardless of the user's view-switching model or download rate. In addition, we demonstrate that the proposed method does not cause playback stalls irrespective of high and low motion video contents.