In multiview applications, camera views can be used as reference views to synthesize additional virtual viewpoints, allowing users to freely navigate within a 3D scene. However, bandwidth constraints may restrict the number of reference views sent to clients, limiting the quality of the synthesized viewpoints. In this work, we study the problem of in-network reference view synthesis aimed at improving the navigation quality at the clients. We consider a distributed cloud network architecture, where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. We argue that, in case of limited bandwidth from the cloudlet to the users, re-sampling at the couldlet the viewpoints of the 3D scene (i.e., synthesizing novel virtual views in the cloudlets to be used as new references to the decoder) is beneficial compared to mere subsampling of the original set of camera views. We therefore cast a new reference view selection problem that seeks the subset of views minimizing the distortion over a view navigation window defined by the user under bandwidth constraints. We prove that the problem is NP-hard, and we propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem under general assumptions that cover most of the multiview scenarios in practice. Simulation results confirm the performance gain offered by virtual view synthesis in the network.