Abstract
Apart from video rate (or requested bitrate), Mean Opinion Score (MOS) has increasingly become a primary term representing Quality of Experience (QoE) in HTTP adaptive streaming (HAS). By monitoring this metric, QoE management can effectively maximize QoE for the users. However, due to various behaviors of particular commercial HAS players, deciding an appropriate monitoring interval has not been fully investigated yet. In this paper, an optimal interval is proposed to be equal to duration of a video chunk in order to aid service managers in early detecting QoE deterioration and limiting the probability of video rate deterioration. The optimal monitoring interval is evaluated by comparing with other values of interval in terms of ratio of video rate deterioration. Furthermore, MOS-based QoE monitoring method which takes into account the proposed interval is thus compared with video rate based monitoring method. The results show that with optimal interval, MOS monitoring guarantees a low ratio of video rate deterioration (around 10% for buffering state and 40% for steady state) and small average CPU Load (about 11.45%).
Highlights
Video has become the most dominant application on the Internet
The results show that with optimal interval, Mean Opinion Score (MOS) monitoring guarantees a low ratio of video rate deterioration and small average CPU Load
The results show that playback buffer should be considered as a milestone to decide the monitoring interval of estimated MOS
Summary
Video has become the most dominant application on the Internet. According to [1], video traffic is predicted to account for about 90 percent of global IP traffic by 2019. In HTTP adaptive streaming, video rate and playback buffer are typically obtained on a chunk-by-chunk basic. As such, they are always observed with long unfixed interval. By using a combination of TCP and HTTP, it becomes a cost-effective technology for delivering video on the Internet Important, it has no difficulties traversing firewalls and NAT devices. Once a certain amount of content is either downloaded or the playback buffer reaches a predefined target (let say as Bmax ), the steady state (or periodic download) is activated In this phase, HAS player attempts to maximize video rate by keeping playback buffer stable at Bmax. Note that when stimulus occurs (in this paper, a stimulus is understood as available bandwidth reduction), the buffering state will be re-activated
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
