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360-degree video becomes increasingly popular among users. In the currentnetwork bandwidth, serving high resolution 360 degree video to users is quitedifficult. Most of the work has been devoted to the prediction of userviewports or tile-based adaptive algorithms. However, it is difficult topredict user viewports more accurately using only information such as user'shistorical viewports or video saliency maps. In this paper, we propose aviewport adaptive 360-degree video streaming method based on tile priority(VATP360), which tries to balance between the performance and the overhead. Theproposed VATP360 consists of three main modules: viewport prediction, tilepriority classification and bitrate allocation. In the viewport predictionmodule, object motion trajectory and predicted user's region-of-interest (ROI)are used to achieve accurate prediction of the user's future viewport. Then,the predicted viewport, along with the object motion trajectory, are fed intothe proposed tile priority classification algorithm to assign differentpriorities to tiles, which would reduce the computational complexity of thebitrate allocation module. Finally in the bitrate allocation stage, weadaptively assign bitrates to tiles of different priority by reinforcementlearning. Experimental results on publicly available datasets have demonstratedthe effectiveness of the proposed method.

VATP360: Viewport Adaptive 360-Degree Video Streaming based on Tile Priority