# Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming

arXiv: https://arxiv.org/abs/2409.17596 · HTML: https://mlsanity.com/paper/2409.17596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 88 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment](https://mlsanity.com/paper/2103.07666/) (2021)
- [Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks](https://mlsanity.com/paper/2002.09140/) (2020)
- [BOLA: Near-Optimal Bitrate Adaptation for Online Videos](https://mlsanity.com/paper/1601.06748/) (2016)
- [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022)
- [VDPVE: VQA Dataset for Perceptual Video Enhancement](https://mlsanity.com/paper/2303.09290/) (2023)
- [Predicting the Quality of Compressed Videos with Pre-Existing Distortions](https://mlsanity.com/paper/2004.02943/) (2020)
- [No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness](https://mlsanity.com/paper/2102.11393/) (2021)
- [LIQA: Lifelong Blind Image Quality Assessment](https://mlsanity.com/paper/2104.14115/) (2021)
- [SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment](https://mlsanity.com/paper/1804.04813/) (2018)
- [Tensor Oriented No-Reference Light Field Image Quality Assessment](https://mlsanity.com/paper/1909.02358/) (2019)
- [Towards Perceptually Optimized End-to-end Adaptive Video Streaming](https://mlsanity.com/paper/1808.03898/) (2018)
- [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)
- [A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming](https://mlsanity.com/paper/2303.09818/) (2023)
- [A Knowledge-Driven Quality-of-Experience Model for Adaptive Streaming Videos](https://mlsanity.com/paper/1911.07944/) (2019)

## Built on by (most cited first, 1 of 1)

- [QoS-QoE Translation with Large Language Model](https://mlsanity.com/paper/2604.08703/) (2026)

## Abstract (arXiv)

In recent years, live video streaming has gained widespread popularity across various social media platforms. Quality of experience (QoE), which reflects end-users' satisfaction and overall experience, plays a critical role for media service providers to optimize large-scale live compression and transmission strategies to achieve perceptually optimal rate-distortion trade-off. Although many QoE metrics for video-on-demand (VoD) have been proposed, there remain significant challenges in developing QoE metrics for live video streaming. To bridge this gap, we conduct a comprehensive study of subjective and objective QoE evaluations for live video streaming. For the subjective QoE study, we introduce the first live video streaming QoE dataset, TaoLive QoE, which consists of 42 source videos collected from real live broadcasts and 1,155 corresponding distorted ones degraded due to a variety of streaming distortions, including conventional streaming distortions such as compression, stalling, as well as live streaming-specific distortions like frame skipping, variable frame rate, etc. Subsequently, a human study was conducted to derive subjective QoE scores of videos in the TaoLive QoE dataset. For the objective QoE study, we benchmark existing QoE models on the TaoLive QoE dataset as well as publicly available QoE datasets for VoD scenarios, highlighting that current models struggle to accurately assess video QoE, particularly for live content. Hence, we propose an end-to-end QoE evaluation model, Tao-QoE, which integrates multi-scale semantic features and optical flow-based motion features to predicting a retrospective QoE score, eliminating reliance on statistical quality of service (QoS) features.