Feature-based prediction of streaming video QoE: Distortions, stalling and memory

Abstract Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic. The available bandwidths of modern wireless networks are often unstable, leading to difficulties in delivering smooth, high-quality video. Streaming service providers such as Netflix and YouTube attempt to adapt their systems to adjust in response to these bandwidth limitations by changing the video bitrate or, failing that, allowing playback interruptions (stalling). Being able to predict end users’ quality of experience (QoE) resulting from these adjustments could lead to perceptually-driven network resource allocation strategies that would deliver streaming content of higher quality to clients, while being cost effective for providers. To this end, a number of QoE predictors have been developed, but they do not always capture the interplay between video quality and stalling. Towards more effectively predicting user QoE, we have developed a QoE prediction model called Video Assessment of TemporaL Artifacts and Stalls (Video ATLAS), which is a feature-based approach that combines a number of QoE-related features, including perceptually-relevant quality features, stalling-aware features and memory-driven features to make QoE predictions. We evaluated Video ATLAS on the recently designed LIVE-Netflix Video QoE Database which consists of practical playout patterns, where the videos are afflicted by both quality changes and stalling events, and found that it provides improved performance over state-of-the-art video quality metrics while generalizing well on a different dataset. The proposed algorithm is made publicly available at http://live.ece.utexas.edu/research/VideoATLAS/vatlas_index.html .