A Knowledge-Driven Quality-of-Experience Model for Adaptive Streaming Videos
This work addresses the problem of accurate QoE prediction for adaptive video streaming, which is crucial for improving user experience in multimedia applications, and represents a novel method for a known bottleneck.
The paper tackles the challenge of predicting Quality-of-Experience (QoE) for adaptive streaming videos by proposing a knowledge-driven model (KSQI) that integrates prior knowledge and human data, achieving state-of-the-art performance on four benchmark datasets.
The fundamental conflict between the enormous space of adaptive streaming videos and the limited capacity for subjective experiment casts significant challenges to objective Quality-of-Experience (QoE) prediction. Existing objective QoE models exhibit complex functional form, failing to generalize well in diverse streaming environments. In this study, we propose an objective QoE model namely knowledge-driven streaming quality index (KSQI) to integrate prior knowledge on the human visual system and human annotated data in a principled way. By analyzing the subjective characteristics towards streaming videos from a corpus of subjective studies, we show that a family of QoE functions lies in a convex set. Using a variant of projected gradient descent, we optimize the objective QoE model over a database of training videos. The proposed KSQI demonstrates strong generalizability to diverse streaming environments, evident by state-of-the-art performance on four publicly available benchmark datasets.