# A review on Machine Learning based User-Centric Multimedia Streaming Techniques arXiv: https://arxiv.org/abs/2411.15801 ยท HTML: https://mlsanity.com/paper/2411.15801/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 151 references that are in the map. - First posted on arXiv 24 Nov 2024. ## Builds on (most cited first, 30 of 31) - [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018) - [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021) - [Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception](https://mlsanity.com/paper/2108.08505/) (2021) - [Video Super Resolution Based on Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2007.12928/) (2020) - [ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction](https://mlsanity.com/paper/2010.13715/) (2020) - [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021) - [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022) - [Subjective and Objective Analysis of Streamed Gaming Videos](https://mlsanity.com/paper/2203.12824/) (2022) - [A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment](https://mlsanity.com/paper/1705.05126/) (2017) - [Assessing Visual Quality of Omnidirectional Videos](https://mlsanity.com/paper/1709.06342/) (2017) - [Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning](https://mlsanity.com/paper/1908.02270/) (2019) - [PEA265: Perceptual Assessment of Video Compression Artifacts](https://mlsanity.com/paper/1903.00473/) (2019) - [DSSIM: a structural similarity index for floating-point data](https://mlsanity.com/paper/2202.02616/) (2022) - [Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos](https://mlsanity.com/paper/2305.17260/) (2023) - [One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment](https://mlsanity.com/paper/2304.03412/) (2023) - [Pano: Optimizing 360deg Video Streaming with a Better Understanding of Quality Perception](https://mlsanity.com/paper/1911.04139/) (2019) - [HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos](https://mlsanity.com/paper/2304.13162/) (2023) - [Enhancing VMAF through New Feature Integration and Model Combination](https://mlsanity.com/paper/2103.06338/) (2021) - [A Ground-Truth Data Set and a Classification Algorithm for Eye Movements in 360-degree Videos](https://mlsanity.com/paper/1903.06474/) (2019) - [Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach](https://mlsanity.com/paper/1807.07126/) (2018) - [Modeling the Time-varying Subjective Quality of HTTP Video Streams with Rate Adaptations](https://mlsanity.com/paper/1311.6441/) (2013) - [A Taxonomy and Dataset for 360deg Videos](https://mlsanity.com/paper/1905.03823/) (2019) - [Convolutional Neural Networks for Continuous QoE Prediction in Video Streaming Services](https://mlsanity.com/paper/2003.08574/) (2020) - [A FUNQUE Approach to the Quality Assessment of Compressed HDR Videos](https://mlsanity.com/paper/2312.08524/) (2023) - [Probabilistic Tile Visibility-Based Server-Side Rate Adaptation for Adaptive 360-Degree Video Streaming](https://mlsanity.com/paper/1906.08575/) (2019) - [A Brief Survey on Adaptive Video Streaming Quality Assessment](https://mlsanity.com/paper/2202.12987/) (2022) - [FOVQA: Blind Foveated Video Quality Assessment](https://mlsanity.com/paper/2106.13328/) (2021) - [Towards a QoE Model to Evaluate Holographic Augmented Reality Devices](https://mlsanity.com/paper/2212.13842/) (2022) - [Optimal Network-Assisted Multi-user DASH Video Streaming](https://mlsanity.com/paper/1703.03214/) (2017) - [Viewport-Aware Dynamic 360deg Video Segment Categorization](https://mlsanity.com/paper/2105.01701/) (2021) ## Built on by (most cited first, 1 of 1) - [HCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression](https://mlsanity.com/paper/2508.02051/) (2025) ## Abstract (arXiv) The multimedia content and streaming are a major means of information exchange in the modern era and there is an increasing demand for such services. This coupled with the advancement of future wireless networks B5G/6G and the proliferation of intelligent handheld mobile devices, has facilitated the availability of multimedia content to heterogeneous mobile users. Apart from the conventional video, the 360o videos have gained popularity with the emerging virtual reality applications. All formats of videos (conventional and 360o) undergo processing, compression, and transmission across dynamic wireless channels with restricted bandwidth to facilitate the streaming services. This causes video impairments, leading to quality degradation and poses challenges in delivering good Quality-of-Experience (QoE) to the viewers. The QoE is a prominent subjective quality measure to assess multimedia services. This requires end-to-end QoE evaluation. Efficient multimedia streaming techniques can improve the service quality while dealing with dynamic network and end-user challenges. A paradigm shift in user-centric multimedia services is envisioned with a focus on Machine Learning (ML) based QoE modeling and streaming strategies. This survey paper presents a comprehensive overview of the overall and continuous, time varying QoE modeling for the purpose of QoE management in multimedia services. It also examines the recent research on intelligent and adaptive multimedia streaming strategies, with a special emphasis on ML based techniques for video (conventional and 360o) streaming. This paper discusses the overall and continuous QoE modeling to optimize the end-user viewing experience, efficient video streaming with a focus on user-centric strategies, associated datasets for modeling and streaming, along with existing shortcoming and open challenges.