# A Brief Survey on Adaptive Video Streaming Quality Assessment

arXiv: https://arxiv.org/abs/2202.12987 · HTML: https://mlsanity.com/paper/2202.12987/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale](https://mlsanity.com/paper/1305.0510/) (2013)
- [Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory](https://mlsanity.com/paper/1703.00633/) (2017)
- [Assessing the Quality-of-Experience of Adaptive Bitrate Video Streaming](https://mlsanity.com/paper/2008.08804/) (2020)
- [Modeling the Time-varying Subjective Quality of HTTP Video Streams with Rate Adaptations](https://mlsanity.com/paper/1311.6441/) (2013)
- [Survey on QoEQoS Correlation Models For Multimedia Services](https://mlsanity.com/paper/1306.0221/) (2013)
- [Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment](https://mlsanity.com/paper/2009.03411/) (2020)

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

- [Perceptual Video Quality Assessment: A Survey](https://mlsanity.com/paper/2402.03413/) (2024)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [Representation Recycling for Streaming Video Analysis](https://mlsanity.com/paper/2204.13492/) (2022)

## Abstract (arXiv)

Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.