One of recent proposals on standardizing quality of user experience (QoE) of video streaming over mobile network is is video Mean Opinion Score (vMOS), which can model QoE of video streaming in 5 discrete grades. However, there are few studies on quantifying vMOS and investigating the relationship between vMOS and other Quality of Service (QoS) parameters. In this paper, we address this concern by proposing a novel data analytical framework based on video streaming QoE data. In particular, our analytical model consists of K-means clustering and logistic regression. This model integrates the benefits of both these two models. Moreover, we conduct extensive experiments on realistic dataset and verify the accuracy of our proposed model. The results show that our proposed framework outperforms other existing methods in terms of prediction accuracy. Moreover, our results also show that vMOS is essentially affected by many QoS parameters such as initial buffering latency, stalling ratio and stalling times. Our results offer a number of insights in improving QoE of video streaming over mobile networks.
We publish the dataset used in this paper to promote the research in this area. The dataset can be found in https://github.com/henryRDlab/VMOS.