Yu Guan, Chengyuan Zheng, Xinggong Zhang, Zongming Guo, and Junchen Jiang.of the 8th ACM on Multimedia Systems Conference (MMSys) (Taipei, Taiwan). Xavier Corbillon, Francesca De Simone, and Gwendal Simon.Deep reinforcement learning: A brief survey. Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath.By conducting extensive trace-driven evaluations, we compare the performance of our proposed SR360 with other state-of-the-art methods and the results show that SR360 significantly outperforms other methods by at least 30% on average under different QoE metrics. We adopt the theory of deep reinforcement learning (DRL) to make a set of decisions jointly, including user FoV prediction, bitrate allocation and SR enhancement. In the SR360 framework, a video tile with low resolution can be boosted to a video tile with high resolution using SR techniques at the client side. The basic idea of our proposed SR360 framework is to utilize abundant computation resources on the user devices to trade off a reduction of network bandwidth. In this paper, we re-design the 360-degree video streaming systems by leveraging the technique of super-resolution (SR). However, it is difficult to perform accurate FoV prediction due to diverse user behaviors and time-varying network conditions. Given the limited network bandwidth, it is a common approach to only stream video tiles in the user's Field-of-View (FoV) with high quality. 360-degree videos have gained increasing popularity due to its capability to provide users with immersive viewing experience.
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