# Modeling the Time-varying Subjective Quality of HTTP Video Streams with Rate Adaptations

arXiv: https://arxiv.org/abs/1311.6441 · HTML: https://mlsanity.com/paper/1311.6441/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2013.

## Built on by (most cited first, 4 of 4)

- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [A Brief Survey on Adaptive Video Streaming Quality Assessment](https://mlsanity.com/paper/2202.12987/) (2022)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [Satellite Streaming Video QoE Prediction: A Real-World Subjective Database and Network-Level Prediction Models](https://mlsanity.com/paper/2410.13952/) (2024)

## Abstract (arXiv)

Newly developed HTTP-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' Quality of Experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer-duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for on-line TVSQ prediction in HTTP based streaming.