Optimizing Adaptive Video Streaming: A Comprehensive Review of Particle Swarm Optimization for Dynamic Bitrate Adaptation

Adaptive video streaming has become integral in delivering high-quality multimedia content over dynamic network conditions. This review explores the application of Particle Swarm Optimization (PSO) algorithms for dynamically adjusting video bitrates, considering factors such as network conditions, user preferences, and content characteristics. The paper begins by providing an overview of adaptive video streaming challenges and existing approaches. Subsequently, it delves into the principles of PSO, highlighting its suitability for dynamic and non-linear optimization scenarios. The review summarizes related work in adaptive video streaming, emphasizing studies that employ PSO or similar algorithms for bitrate adaptation. The specific application of PSO in bitrate adaptation is detailed, outlining its role in optimizing video quality based on real-time network conditions and user requirements. Performance metrics relevant to PSO-based bitrate adaptation, such as Quality of Experience (QoE) and bitrate switching frequency, are discussed. The paper then presents an analysis of the strengths and limitations of existing PSO-based approaches, paving the way for future improvements. In conclusion, this comprehensive review underscores the significance of Particle Swarm Optimization in addressing bitrate adaptation challenges in adaptive video streaming. It provides insights into the potential impact of PSO algorithms and suggests future research directions, positioning itself as a valuable resource for researchers and practitioners in the field