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首页> 外文期刊>Journal of Cloud Computing: Advances, Systems and Applications >Towards secure and network state aware bitrate adaptation at IoT edge
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Towards secure and network state aware bitrate adaptation at IoT edge

机译：在IOT边缘的安全和网络状态感知Bitrate适应

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摘要

Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
机译：视频流在IOT系统中至关重要，从而实现各种应用，例如交通监控和健康。传统的自适应比特率流（ABR）算法主要专注于改善网络条件相对稳定的互联网视频流质量。然而，这些方法遭受了IOT边缘的性能下降。在物联网系统中，无线信道容易发生干扰和恶意攻击，这显着影响了视频流应用的体验质量（QoE）。在本文中，我们提出了一种安全和网络状态感知解决方案，SASA，以解决这些挑战。我们首先在增加比特率时研究缓冲级约束。然后，我们分析吞吐量估计在比特率决策中的影响。基于这些结果，SASA旨在包括离线和在线阶段。在离线阶段，SASA预先计算各种网络条件下的ABR算法的最佳配置。在在线阶段，SASA采用在线贝叶斯变换点检测方法来检测网络变化，并应用预先计算的配置以进行比特率决策。我们实现SASA并使用429个真实网络迹线评估其性能。我们展示了SASA优于最先进的ABR算法，例如通过广泛的实验在物联网环境中获得robustMPC和Oboe。

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