论文标题
ECAS-ML：边缘计算辅助自适应方案，用于HTTP自适应流的机器学习
ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming
论文作者
论文摘要
随着移动网络中的视频流流量的增加，通过利用边缘计算支持来改善内容交付过程变得至关重要。在边缘节点上，我们可以更好地理解网络行为以及对无线电和播放器指标的访问，可以部署自适应比特率（ABR）算法。在这项工作中，我们介绍了通过机器学习的HTTP自适应流的ECAS-ML，边缘辅助自适应方案。 ECAS-ML专注于管理比特率，细分开关和摊位之间的权衡，以实现更高的经验（QOE）。为此，我们使用机器学习技术来分析无线电吞吐量轨迹并预测算法的最佳参数以实现更好的性能。结果表明，ECAS-ML胜过其他基于客户端和基于边缘的ABR算法。
As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches, and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.
