Adaptive Video Streaming with AI-Based Optimization for Dynamic Network Conditions

TL;DR

This paper presents an AI-driven adaptive video streaming system that dynamically adjusts quality and buffer size based on network conditions, significantly reducing buffering and enhancing user experience.

Contribution

It introduces a novel AI-based framework that optimizes video quality and buffer management in real-time for variable network environments.

Findings

Reduces buffering events during streaming

Increases video quality through AI optimization

Improves user experience with adaptive streaming

Abstract

The increase in video streaming has presented a challenge of handling stream request effectively, especially over networks that are variable. This paper describes a new adaptive video streaming architecture capable of changing the video quality and buffer size depending on the data and latency of streamed video. For video streaming VLC media player was used where network performance data were obtained through Python scripts with very accurate data rate and latency measurement. The collected data is analyzed using Gemini AI, containing characteristics of the machine learning algorithm that recognizes the best resolution of videos and the buffer sizes. Through the features of real-time monitoring and artificial intelligence decision making, the proposed framework improves the user experience by reducing the occurrence of buffering events while at the same time increasing the video quality. Our findings therefore confirm that the proposed solution based on artificial intelligence increases video quality and flexibility. This study advances knowledge of adaptive streaming and offers an argument about how intelligent datadriven approaches and AI may be useful tools for enhancing the delivery of video in practical environments.