Application of machine learning for Managing High-Definition Video Streaming
Keywords:
machine learning, adaptive bitrate streaming, quality of experience, reinforcement learning, bandwidth prediction, unsupervised learning, video streaming, VR/360° video, energy-aware streaming, network optimizationAbstract
The paper explores the idea of using machine learning for high-definition video streaming. Formats like 4K, 8K, and VR make this task harder because they need more stable delivery. The purpose is to review the work from 2021 to 2024 and apply different approaches — forecasting models, reinforcement learning, and some unsupervised methods — to see how they affect the stability and quality of adaptive bitrate streaming. The review covers a variety of research: forecasting with BiLSTM–CNN and GRU models, reinforcement learning systems like DQNReg, DeepVR, or GreenABR, and clustering techniques for monitoring QoE. The main results indicate that machine learning solutions outperform older ABR rules: fewer stalls, smoother quality, higher QoE, and in some cases lower power use on mobile devices. At the same time, the models are often heavy to run and may not generalize well outside the training data. The article is meant for researchers and engineers working on video delivery and network optimization, and points to where ML-based streaming could be applied in practice.
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