In multiview 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 multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We then propose a problem formulation for foresighted optimization of scheduling policies, which adapt to temporal variations in the scene content. Furthermore, we propose a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted for optimized reconstruction quality. Simulation results show the gain of our scheduling algorithm when correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in dynamic scenarios where the level of correlation varies rapidly with time.