Concerning the fact that the existing rate adaptation algorithms based on Dynamic Adaptive Streaming over HTTP (DASH) have frequent bitrate switching and low average bitrate in wireless network, a Rate Smooth Switching (RSS) algorithm based on DASH standard was proposed. Firstly, a sliding window was used by the bandwidth detection mechanism of the algorithm to sample the download speed of historical segments to calculate the bandwidth offset coefficient, the fluctuation of the bandwidth was initially determined according to the value of offset coefficient, and the situation of the fluctuation was further determined whether there was a consistent variation trend, thereby distinguishing continuous variation and short-term jitter of the bandwidth, and the bandwidth prediction value corresponding to each circumstance was calculated. Secondly, with bandwidth fluctuation, buffer occupancy and variation, bandwidth prediction value considered, the rate decision model of the algorithm adopted Fast Buffering (FB), Slow Switching (SS), Fast Rising (FR), Limited Declining (LD), Stable Holding (SH) strategies and sleeping mechanism to dynamically control the video bitrate selection process. The experimental results show that compared with fuzzy-based DASH rate adaptation algorithm and modulated throughput driven rate adaptation algorithm, the proposed algorithm can not only increase the bitrate to optimum level in the shortest time at the beginning of video playback to improve the average bitrate, but also minimize the number of bitrates' switching in the case of sudden change and frequent fluctuation of bandwidth, thus obtaining a good quality of experience for wireless video users.

Due to the inherent randomness of bandwidth, the existing rate adaptation algorithms based on Dynamic Adaptive Streaming over Hyper Text Transfer Protocol (DASH) fail to make a balance between playback fluency and video quality. Concerning the above problem, a State machine-based DASH (SDASH) algorithm was proposed to analyze and control the rate switching process. Firstly, the influence factors of client's Quality of Experience (QoE) were fully considered and numerically analyzed. Secondly, six bitrate states were proposed according to the influence factors, and the relations between the video bitrate and the changes in influence factors' values were used as the state transition conditions. Finally, while the playback buffer and the bitrate deviation ratio satisfying the threshold condition, the video bitrate was switched to a rate level which has the relatively optimum overall performance of playback fluency and video quality. The experimental results demonstrate that the proposed algorithm can not only improve the video bitrate compared with the fuzzy-based DASH adaptation algorithm but also avoid bitrate plunging, thus reaching a balance between playback fluency and video quality, and leading to an improvement of QoE.