# A Knowledge-Driven Quality-of-Experience Model for Adaptive Streaming Videos

arXiv: https://arxiv.org/abs/1911.07944 · HTML: https://mlsanity.com/paper/1911.07944/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming](https://mlsanity.com/paper/2409.17596/) (2024)
- [TSKAN: Interpretable Machine Learning for QoE modeling over Time Series Data](https://mlsanity.com/paper/2509.20595/) (2025)
- [HiVid: LLM-Guided Video Saliency For Content-Aware VOD And Live Streaming](https://mlsanity.com/paper/2602.14214/) (2026)

## Abstract (arXiv)

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.