Boosting Video Streaming Efficiency Through DQN Machine Learning Algorithm-Based Resource Allocation

Video streaming has become increasingly popular with the proliferation of online platforms and the widespread availability of high-speed Internet connections. However, delivering high-quality video content over limited network resources remains a challenge. In this paper, we propose a novel approach to boost video streaming efficiency through machine learning-based resource allocation. Our approach leverages the power of machine learning algorithms to dynamically allocate network resources based on various factors such as network conditions, video content characteristics, and user preferences. By intelligently adapting the resource allocation in real-time, we aim to optimize video streaming performance and enhance the overall user experience. The experimental results demonstrate that our machine learning-based resource allocation approach outperforms existing methods in terms of key performance metrics such as video quality, buffering time, and overall user satisfaction. Through intelligent resource allocation, our approach effectively mitigates video stalling and buffering issues, leading to smoother video playback and reduced quality degradation during adverse network conditions.