Mobile 360-degree video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient and variable wireless network bandwidth. Recently, saliency-driven 360-degree streaming overcomes the buffer size limitation of head movement trajectory (HMT)-driven solutions and thus strikes a better balance between video quality and rebuffering. However, inaccurate network estimations and intrinsic saliency bias still challenge saliency-based streaming approaches, limiting further QoE improvement. To address these challenges, we design a robust saliency-driven quality adaptation algorithm for 360-degree video streaming, RoSal360. Specifically, we present a practical, tile-size-aware deep neural network (DNN) model with a decoupled self-attention architecture to accurately and efficiently predict the transmission time of video tiles. Moreover, we design a reinforcement learning (RL)-driven online correction algorithm to robustly compensate the improper quality allocations due to saliency bias. Through extensive prototype evaluations over real wireless network environments including commodity WiFi, 4G/LTE, and 5G links in the wild, RoSal360 significantly enhances the video quality and reduces the rebuffering ratio, thereby improving the viewer QoE, compared to the state-of-the-art algorithms.

Mobile 360° video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient wireless network bandwidth. The state-of-the-art solutions are limited by the temporal correlation assumption. Recent studies are aware of the potential of saliency to further QoE improvement, but several fundamental challenges about saliency judgment, saliency acquirement, and quality adaptation are still not fully addressed. To solve these challenges, we present SalientVR, a saliency-driven mobile 360° video streaming system integrated with gaze information. We design (i) a precise gaze-driven saliency judging criterion for mobile VR viewers, (ii) two pragmatic gaze-driven, tile-level saliency acquiring methods based on cross-user similarity and a specific content-aware deep neural network respectively, and (iii) a lightweight saliency-aware quality adaptation algorithm with a motion-assisted online correction, which is robust to wireless bandwidth vagaries and saliency bias. Moreover, we contribute a gaze-annotated dataset and a gaze-driven quality assessment metric for 360° videos. By extensive prototype evaluations (based on dataset tests and user studies), compared to alternatives, SalientVR significantly enhances the video quality and reduces the rebuffering ratio over 4G/LTE network emulations and in the wild, which achieves a 43.68% QoE improvement.