Efficient Caching for Multi-view 3D Videos with Depth-Image-Based Rendering

Due to the emergence of mobile 3D and VR devices, multi-view 3D videos are expected to play increasingly important roles shortly. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires a larger storage space. Nevertheless, efficient caching of multi-view 3D videos in a proxy has not been explored in the literature. In this thesis, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D. We then formulate a new cache replacement problem, named View Selection and Cache Operation (VSCO), and find the optimal policy based on Markov Decision Process. In addition, we devise an efficient and effective algorithm, named Efficient View Exploration Algorithm (EVEA), to solve the problem in large cases. Simulation results manifest that the proposed algorithm can significantly improve the cache hit rate and reduce the total cost compared with the previous renowned cache replacement algorithms.