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In multi-view applications, multiple cameras acquire the same scene from different viewpoints and produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multi-view data. In this paper, the authors propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras is transmitted over a bottleneck channel to clients that reconstruct multi-view images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. They then propose a problem formulation for foresighted optimization of scheduling policies, which adapt to temporal variations in the scene content.
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