Dynamic adaptive streaming over HTTP （DASH） has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data- pushing in HTFP/2 is emerging as a promising technology....Dynamic adaptive streaming over HTTP （DASH） has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data- pushing in HTFP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTFP/2 with the aim to improve the quality of experience （QoE） of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transi- tion probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.展开更多
Benefiting from the improvements of Internet infrastructure and video coding technology, online video services are becoming a new favorite form of video entertainment.However, most of the existing video quality assess...Benefiting from the improvements of Internet infrastructure and video coding technology, online video services are becoming a new favorite form of video entertainment.However, most of the existing video quality assessment methods are designed for broadcasting/cable televisions and it is still an open issue how to assess and measure the quality of online video services. In this paper, we survey the state-of-the-art video streaming technologies, and present a framework of quality assessment and measurement for Internet video streaming. This paper introduces several metrics for user's quality of experience(QoE).These QoE metrics are classified into two categories: objective metrics and subjective metrics. It is different for service participators to measure objective and subjective metrics.The QoE measurement methodologies consist of client-side, server-side, and in-network measurement.展开更多
基金supported in part by China“973”Program under Grant No.2014CB340303”ZTE Industry-Academia-Research Cooperation Funds
文摘Dynamic adaptive streaming over HTTP （DASH） has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data- pushing in HTFP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTFP/2 with the aim to improve the quality of experience （QoE） of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transi- tion probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.
基金supported by National Key R&D Program of China No.2018YFB0803702Beijing Culture Development Funding under Grant No.2016-288Toutiao Funding No.ZN20171224003
文摘Benefiting from the improvements of Internet infrastructure and video coding technology, online video services are becoming a new favorite form of video entertainment.However, most of the existing video quality assessment methods are designed for broadcasting/cable televisions and it is still an open issue how to assess and measure the quality of online video services. In this paper, we survey the state-of-the-art video streaming technologies, and present a framework of quality assessment and measurement for Internet video streaming. This paper introduces several metrics for user's quality of experience(QoE).These QoE metrics are classified into two categories: objective metrics and subjective metrics. It is different for service participators to measure objective and subjective metrics.The QoE measurement methodologies consist of client-side, server-side, and in-network measurement.
