Research On Video-on-demand Technology Based On DASH In High-speed Mobile Scene

With the continuous development of the mobile Internet and the popularity of smart phones in recent years,more and more people have begun to use mobile streaming software.In daily use,when users play videos,the current mainstream adaptive bitrate algorithm can play videos smoothly and with high quality.Moreover,people also expect to have a stable and smooth playback experience even when the network is not stable.In China,the main means of transportation for people to travel across cities are trains such as bullet train.While the train is running,video playback often freezes.The main reason is that when the train is running at high speed,it will quickly pass through areas covered by various signal strengths,and some areas have poor signals,or even there are also areas with no signal at all.For example,in tunnel scenarios,many tunnels lack network base station installations inside,so the signal quality in this area is poor.The complex and extremely changeable network has led to the poor performance of the current mainstream online adaptive bitrate algorithm during the train running.Improper bitrate selection will often cause playback freezes,which greatly affects the user’s viewing experience.Therefore,there are the following difficulties in maintaining the smooth playback of online video on a high-speed moving train:(1)The network fluctuates violently during the high-speed running of the train,and it is difficult to predict.(2)The performance gap between different mobile devices is large,so the algorithm is required to be universal and should not be too complicated.(3)The train enters a weak signal area,and video buffering cannot be performed normally.Based on the above problems,this paper proposes two algorithms to improve the problem that is easy to cause stuttering during the train running.The first algorithm is a hybrid control ABR algorithm based on multi-threshold strategy,which improves the problem that the current mainstream QoE-oriented ABR algorithm is prone to stuttering during high-speed train driving.In order to improve video quality,algorithms of this kind aim to increase the bitrate as much as possible by reducing the buffering amount.This kind of algorithm can maintain a high QoE when the network is relatively stable,and when the network fluctuates violently,such algorithms are prone to lag.This algorithm switches the bitrate selection strategy by setting multiple buffer thresholds,and at the same time detects changes in bandwidth to control the switching frequency of video quality,thereby reducing the number and duration of network freezes in weak network environments.Experimental results show that the algorithm can greatly reduce the number of network freezes and the duration of network freezes caused by weak network environments.The second algorithm combines the multi-threshold strategy with Model Predictive Control.By monitoring the state of the buffer,different QoE models are used at different stages of the buffer,and different QoE models are used to drive the MPC controller.Compared to the first algorithm,this algorithm maintains a low freeze rate while minimizing the number of bitrate switches.Additionally,it achieves an average overall QoE improvement of 14%.