Quality metric to assess video streaming service over TCP considering temporal location of pauses

Abstract

There is a wide range of video services over complex transmission networks, and in some cases end users fail to receive an acceptable quality level. In this paper, the different factors that degrade users' quality of experience (QoE) in video streaming service that use TCP as transmission protocol are studied. In this specific service, impairment factors are: number of pauses, their duration and temporal location. In order to measure the effect that each temporal segment has in the overall video quality, subjective tests. Because current subjective test methodologies are not adequate to assess video streaming over TCP, some recommendations are provided here. At the application layer, a customized player is used to evaluate the behavior of player buffer, and consequently, the end user QoE. Video subjective test results demonstrate that there is a close correlation between application parameters and subjective scores. Based on this fact, a new metrics named VsQM is defined, which considers the importance of temporal location of pauses to assess the user QoE of video streaming service. A useful application scenario is also presented, in which the metrics proposed herein is used to improve video services 1 .

Impact of video content and transmission impairments on quality of experience

“…The perceptual and attentive impact of delay and jitter in multimedia delivery network is presented in [15] and the result shows that the delay and jitter significantly affect user QoE, and that content variation also affects the user satisfaction. Impact of the number of pauses, their duration and temporal location in TCP transmission protocol is studied in [34] and a new QoE metric for video streaming services is proposed. The network domain parameters: packet loss, delay, jitter, and packet reorder affect the video quality more than video content, though the effect of noise factors like motion, complexity and location also have a significant impact on the perceived quality [10,11,18,31].…”

“…Authors in [14] conclude that the content dependencies and visual attention also have significant influence on user experience, and the impact of codec and network parameters on QoE is dependent on the video content. Moreover, the QoE is also highly correlated with the users preference of content type [34,43].…”

The impact of video-quality-level switching on user quality of experience in dynamic adaptive streaming over HTTP

“…Finally, the MOS value that characterizes the user QoE can be predicted by incorporating SDF into previous QoE models that estimate MOS without taking into account quality degradations due to VQL switching. For example, the video streaming quality metric (VsQM) proposed in [23] provides a model to predict MOS and is specifically useful when pauses exist during video replay. Combining VsQM and SDF, we obtain a model that predicts the overall MOS value by…”

“…Unfortunately, this has not been well accounted for in state-of-the-art DASH algorithms Visual attention, context awareness, and assessment of users' expectations play an essential role in determining the user's QoE. The assessment of QoE should include objective human cognitive aspects and incorporate some valid psychological subjective and social approaches [22]; thus, the study is multi-disciplinary in nature, incorporating psychology, cognitive science, sociology, and information technology [23]. It is worth noting that during the subjective test, the evaluators' attention is also predominantly selective to the video content being watched.…”

Hybrid video quality prediction: reviewing video quality measurement for widening application scope

“…This type of distortion may appear both in TCP and UDP transmission. In [50] the authors propose a metric to compute MOS as a function of the number of pauses, the average length of pauses that happened in the same temporal segment, a weighting factor which represents the degree of degradation that each segment adds to the total video degradation, the time period in seconds of each segment and the number of temporal segments of a video. It is interesting to note that they divide the video into segments and find the weighting factors for each segment solving an equations system.…”