  Interactive multi-view video streaming (IMVS) services permit to remotely
immerse within a 3D scene. This is possible by transmitting a set of reference
camera views (anchor views), which are used by the clients to freely navigate
in the scene and possibly synthesize additional viewpoints of interest. From a
networking perspective, the big challenge in IMVS systems is to deliver to each
client the best set of anchor views that maximizes the navigation quality,
minimizes the view-switching delay and yet satisfies the network constraints.
Integrating adaptive streaming solutions in free-viewpoint systems offers a
promising solution to deploy IMVS in large and heterogeneous scenarios, as long
as the multi-view video representations on the server are properly selected. We
therefore propose to optimize the multi-view data at the server by minimizing
the overall resource requirements, yet offering a good navigation quality to
the different users. We propose a video representation set optimization for
multiview adaptive streaming systems and we show that it is NP-hard. We
therefore introduce the concept of multi-view navigation segment that permits
to cast the video representation set selection as an integer linear programming
problem with a bounded computational complexity. We then show that the proposed
solution reduces the computational complexity while preserving optimality in
most of the 3D scenes. We then provide simulation results for different classes
of users and show the gain offered by an optimal multi-view video
representation selection compared to recommended representation sets (e.g.,
Netflix and Apple ones) or to a baseline representation selection algorithm
where the encoding parameters are decided a priori for all the views.
