ASSESSING QUALITY OF EXPERIENCE FOR ADAPTIVE HTTP VIDEO STREAMING

In this paper, we present a novel subjective quality model for online adaptive movie streaming service. The proposed model considers the Quality of Experience (QoE) of streaming video viewing as a cumulative evaluation process of consecutive segments that compose a story line. Under bandwidth constraint, streaming client may select lower-quality segment, pause playback for re-buffering, or both. The momentary QoE loss at these events are weighted by the content suspension level. Meanwhile such experience penalty remains to influence user's opinion for the rest of the service. If the picture becomes too noisy or the interruptions occur consistently, the user may stop watching. The proposed scheme includes two parts. First, a parametric model estimates the quality loss of a interfered segment based on its network-level packet characteristics. Second, a cumulative function integrates the impact of streaming events. Both steps demand minimum computation and can be updated in real time. A subjective test to train and validate the proposed parametric model is designed and performed. This model is fundamentally different from all existing QoE assessment schemes in that temporally cumulative viewing experience of the users, instead of simple global statistics, is evaluated.