With the rapid development of immersive multimedia technologies, 360-degree video services have quickly gained popularity and how to ensure sufficient spatial presence of end users when viewing 360-degree videos becomes a new challenge. In this regard, accurately acquiring users’ sense of spatial presence is of fundamental importance for video service providers to improve their service quality. Unfortunately, there is no efficient evaluation model so far for measuring the sense of spatial presence for 360-degree videos. In this paper, we first design an assessment framework to clarify the influencing factors of spatial presence. Related parameters of 360-degree videos and head-mounted display devices are both considered in this framework. Well-designed subjective experiments are then conducted to investigate the impact of various influencing factors on the sense of presence. Based on the subjective ratings, we propose a spatial presence assessment model that can be easily deployed in 360-degree video applications. To the best of our knowledge, this is the first attempt in literature to establish a quantitative spatial presence assessment model by using technical parameters that are easily extracted. Experimental results demonstrate that the proposed model can reliably predict the sense of spatial presence.

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 HTTP/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 HTTP/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 transition 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.