mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming

Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based rate adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video rate switching frequency and amplitude, buffer overflow/underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstrate the good performance of the proposed method in both objective and subjective visual quality.