Advancements in Latency Reduction Models for Adaptive Video Streaming: A Comprehensive Review

With the increasing demand for high-quality video content delivery, the optimization of adaptive video streaming systems has become imperative. One critical aspect that significantly influences user experience is latency, encompassing startup delays, rebuffering occurrences, and end-to-end delays. This review paper comprehensively explores the latest advancements in latency reduction models for adaptive video streaming, considering both network-related and algorithmic approaches. The paper delves into mathematical models designed to minimize the delay between content generation and viewer reception, evaluating their efficacy in realworld scenarios. The exploration encompasses protocols such as HTTP/2, QUIC, and Content Delivery Networks, alongside sophisticated adaptive bitrate algorithms leveraging machine learning techniques. Additionally, the paper investigates hybrid models that integrate both network and algorithmic enhancements to achieve comprehensive latency reduction. Evaluation metrics, challenges, and potential future directions are discussed, providing a holistic overview of the current state of research in this critical domain. The insights presented aim to guide researchers, practitioners, and industry professionals in advancing the field of adaptive video streaming for optimal user satisfaction.