Adaptive Video Streaming with AI-Based Optimization for Dynamic Network Conditions
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PDF downloads: 10
Keywords:
Adaptive Video Stream, AI, Datarate, Latency, GiminiAbstract
The increase in video streaming has presented a challenge in handling stream requests
effectively, especially over variable networks. 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
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 data
driven approaches and AI may be useful tools for enhancing the delivery of video in practical
environments.
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