A method of QoE evaluation for adaptive streaming based on bitrate distribution

In this paper, we propose a Quality of Experience (QoE) model for adaptive streaming services based on bitrate distribution to evaluate customers´ subjective perception accurately. Unlike the QoE assessment for constant bitrate (CBR) video services, different bitrate distribution causes significant difference for the QoE of adaptive streaming. Therefore, we design a method based on the Primacy Effect and Recency Effect, a psychological phenomenon that the initial and recent information are more prominent in short-term memory for people, to analyze and quantify the QoE influence caused by bitrate distribution. Besides the bitrate distribution, real-time bitrate and video content types are also mapped into our QoE model to provide great QoE evaluation for adaptive streaming. We evaluate our QoE model via plenty of subjective Mean Opinion Score (MOS) tests, which include 16 test samples with 659 votes. The Pearson Correlation Coefficient between our QoE score and MOS achieves to 0.97, which indicates that our model can evaluate customers´ perception on adaptive streaming quality accurately. Besides, we compare our QoE model to the average QoE evaluation method. Simulation results show that our QoE model is more flexible and powerful to reflect customers´ feeling on adaptive streaming services with various kinds of bitrate distributions.