The Design of Teaching System for College Piano Online Course Considering Network Delay and Bandwidth Optimization

The rapid growth of online piano education emphasizes the need for efficient teaching content transmission, yet challenges such as network latency and bandwidth constraints remain prevalent. This study explores solutions including content delivery networks (CDNs) and adaptive streaming technologies. CDNs minimize delays by distributing servers globally, while adaptive streaming dynamically adjusts video quality based on network conditions. However, issues such as bandwidth variability across regions and congestion during peak hours persist. To address these challenges, we propose a machine learning-based dynamic bandwidth control model. This model predicts network traffic fluctuations to optimize bandwidth allocation and improve transmission efficiency. Additionally, we evaluate the application of a novel algorithm for managing live video streams within the WebRTC framework. By employing proximal policy optimization (PPO), the proposed approach demonstrates superior performance compared to rule-based and traditional learning algorithms. Through experiments, PPO is shown to enhance video smoothness, clarity, and bandwidth prediction accuracy across diverse network conditions. The study also highlights PPO’s robustness and training efficiency, offering valuable insights for optimizing real-time video streaming and improving the delivery of online piano education.