Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning

IF 0.9 Q4 TELECOMMUNICATIONS
L. Srinivasan, Humaira Nishat, S. Shargunam, Deepak Kumar Nayak, K. Janani
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引用次数: 0

Abstract

In this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.
优化实时视频流:将 5G、物联网和云计算与机器学习相结合
在这项研究中,我们结合了 5G、物联网(IoT)和云计算等先进技术,优化了视频直播性能。我们的方法利用随机森林分类器对数据进行分类,准确率达到 99%。对比研究表明,我们提出的技术在优化视频流效果方面优于 RCNN 和 Mask-RCNN 方法。我们的研究表明,我们的方法通过整合机器学习技术有效地提高了视频流的质量。5G、物联网和云计算的结合为向用户提供优化的实时视频流创造了一个强大的环境。这项研究强调了利用前沿技术解决现代视频流系统优化难题的重要性,重点关注当代技术环境下视频流的实时优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
3.10
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