In the realm of 360° video streaming, how to deliver optimal viewing experience to users with minimal bandwidth cost has become an emerging challenge. Our research is driven by a comprehensive analysis of real-world user's head movement datasets of 360° video, revealing significant viewport shifts even during the playback of individual 360° video chunks. However, prevailing 360° video streaming algorithms fail to account for such viewport variations, thereby resulting in a substantial degradation of user experience. To this end, we propose OMMS, a multiple-control-based adaptive 360° video streaming algorithm. OMMS adopts an integrated approach by introducing multiple supplementary controls alongside the main control, enabling adaptation to changes in the user's viewport. Experimental results demonstrate that OMMS yields a significant improvement in video quality by 34%, while effectively reducing bandwidth resource consumption.

Short video streaming has been increasingly popular in recent years. Due to its unique user behavior of watching and sliding, a critical technique issue is to design a preload algorithm deciding which video chunk to download next, bitrate selection and the pause time, in order to improve user experience while reducing bandwidth wastage. However, designing such a preload algorithm is non-trivial, especially taking into account conflicting goals of improving QoE and reducing bandwidth wastage. In this paper, we propose a deep reinforcement learning-based approach to simultaneously decide the aforementioned three decision variables via learning an optimal policy under a complex environment of varying network conditions and unpredictable user behavior. In particular, we incorporate domain knowledge into the decision procedure via action masking to make decisions more transparent, and accelerate the model training. Experimental results validate the proposed approach significantly outperforms baseline algorithms in terms of QoE metrics and bandwidth wastage.

Web real-time communication (WebRTC) employs congestion control to ensure the quality of experience (QoE). Different from congestion control schemes for TCP, WebRTC keeps a low-level playback buffer that considers excessively delayed packets as losses, which makes the congestion control for WebRTC more challenging. Existing heuristic schemes estimate the network conditions based on hand-crafted rules that may be suboptimal, leading to under-utilization or over-utilization of link capacity in many cases. On the other hand, the existing learning-based schemes train a model that acts in a large action space, which is hard to converge to a stable status and has low performance over unpredictable network conditions. In this paper, we propose a hybrid receiver-side congestion control (HRCC) framework, which combines a heuristic congestion control scheme with an RL-Agent that periodically generates a gain coefficient to tune the bandwidth estimated by the heuristic scheme. Extensive simulation experiments demonstrate that the HRCC's RL-Agent effectively tunes the bandwidth estimate of the heuristic scheme. The hybrid scheme achieves higher bandwidth utilization than the fully heuristic scheme with similar queuing delay and packet loss and outperforms the fully RL-based scheme on overall performance.