Abstract
Dynamic adaptive streaming over HTTP (DASH) has become a promising solution for video delivery services over the Internet in the last few years. Currently, several video content providers use the DASH solution to improve the users’ quality of experience (QoE) by automatically switching video quality levels (VQLs) according to the network status. However, the frequency of switching events between different VQLs during a video streaming session may disturb the user’s visual attention and therefore affect the user’s QoE. As one of the first attempts to characterize the impact of VQL switching on the user’s QoE, we carried out a series of subjective tests, which show that there is a correlation between the user QoE and the frequency, type, and temporal location of the switching events. We propose a novel parameter named switching degradation factor (SDF) to capture such correlation. A DASH algorithm with SDF parameter is compared with the same algorithm without SDF. The results demonstrate that the SDF parameter significantly improves the user’s QoE, especially when network conditions vary frequently.
Highlights
IP network uses the concept of best-effort delivery, where the network does not guarantee the data arrival to the end user at the right time and order, depending on the network traffic load
6 Conclusions Existing Dynamic adaptive streaming over HyperText Transfer Protocol (HTTP) (DASH) solutions do not take into account the impact of video quality levels (VQLs) switching on the users’ quality of experience (QoE)
We make one of the first attempts to address this problem through subjective testing, objective modeling, as well as computer and network configurations to create different scenarios that involved DASH algorithms for adaptive streaming
Summary
IP network uses the concept of best-effort delivery, where the network does not guarantee the data arrival to the end user at the right time and order, depending on the network traffic load. Similar to the case in phase 1, in the k-th scenario, the net impact of VQL switching events on the overall user QoE or the desired S factor (denoted by S―D―FðkDÞ ) would be the difference between the mean of the MOS values of all individual VQLs in all the temporal segments (denoted by MOSmk ean ) and the MOS value given to the whole video (denoted by MOSk), such that ð11Þ. With all the parameters wðiTÞ ’s, wðjSÞ’s, and C determined, we can use Equations 3 and 4 to compute the SDF factors as well as the mapped SD F values for the given test scenarios, and SD F can be subsequently employed to predict the drop of MOS value caused purely by VQL switching events. This predicted MOS value, denoted by MOS(P) can be employed by DASH algorithms for adaptive video streaming
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