  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.
