We derive an optimization framework for computing the transmission actions of streaming multi-view content over bandwidth constrained channels. The optimization allows us to determine the decisions of sending the packetized data such that the end-to-end reconstruction quality of the content is maximized, for the given bandwidth resources. We consider two prospective levels at which transmission decisions can be computed, i.e., a view-level or a packet-level policy selection optimization. In addition, two prospective multi-view content representation formats are considered: MVC and video plus depth. In the case of each, we formulate directed graph models that characterize the interdependencies between the data units comprising the content. For the video plus depth format, we develop a novel space-time error concealment strategy that reconstructs the missing content at the client based on the received data units from multiple views. We design multiple techniques to solve the optimization problems of interest either exactly or approximatively, at lower complexity. In conjunction, we derive spatiotemporal models of the reconstruction error for the multi-view content that we employ to reduce the computational requirements of the optimization. We study the performance of our framework via simulation experiments. Significant gains in terms of rate-distortion efficiency are demonstrated against state-of-the-art reference techniques.