Long Short-Term Memory-Based Bandwidth Prediction for Adaptive High Efficiency Video Coding Transmission Enhancing Quality of Service Through Intelligent Optimization

Abstract

With the growing demand for high-quality video streaming, the necessity for efficient techniques to balance video quality and bandwidth has become increasingly critical to ensure a seamless user experience. Existing traditional adaptive streaming methods only react to network fluctuations, which often leads to delays, quality degradation, and buffering. This paper introduces an AI-powered approach for adaptive High Efficiency Video Coding (HEVC) transmission, using a predictive model based on Long Short-Term Memory (LSTM) networks to predict bandwidth variations and proactively adjust encoding parameters. The proposed approach uses historical and real-time network data to anticipate network changes, offering smoother transitions and reducing buffering. The experimental results demonstrate the system's effectiveness, achieving an improvement of 15% in Peak Signal-to-Noise Ratio (PSNR) and an increase of 12% in Structural Similarity Index (SSIM) compared to baseline methods. Additionally, the system reduces buffering events by 25% while improving bitrate stability by 20%, guaranteeing consistent video quality with minimal interruptions. This proactive approach significantly enhances Quality of Service (QoS) by providing stable video quality and uninterrupted streaming, representing a significant advancement in adaptive streaming technologies.