EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines
EAVS: Edge-assisted Adaptive Video Streaming with Fine-grained Serverless Pipelines
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DOI：
10.1109/infocom53939.2023.10229102
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Biao Hou;Song Yang;F. Kuipers;Lei Jiao;Xiao-Hui Fu
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文献类型：
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作者：
Biao Hou;Song Yang;F. Kuipers;Lei Jiao;Xiao-Hui Fu
近年来，视频流媒体逐渐发展成为最流行的互联网应用之一。随着对实时视频流媒体服务的个性化需求迅速增长，最大限度地提高其体验质量（QoE）是一项长期挑战。无服务器计算范式的出现有可能通过其细粒度管理和高度并行的计算结构来应对这一挑战。然而，如何实现和配置无服务器组件以优化视频流服务仍然是模糊的。在本文中，我们提出了EAVS，边缘辅助自适应视频流系统与无服务器管道，便于细粒度管理多个并发视频传输管道。然后，我们设计了一个块级优化方案来解决视频比特率自适应。我们提出了一种基于邻近策略优化（PPO）的深度强化学习（DRL）算法，该算法具有三重剪辑机制，可以有效地进行比特率决策，以获得更好的QoE。最后，我们实现了无服务器视频流系统原型，并评估了EAVS在各种真实世界网络痕迹上的性能。我们的研究结果表明，EAVS显著提高了QoE，降低了视频停顿率，与最先进的解决方案相比，QoE提高了9.1%以上，延迟降低了60.2%。
Recent years have witnessed video streaming gradually evolve into one of the most popular Internet applications. With the rapidly growing personalized demand for real-time video streaming services, maximizing their Quality of Experience (QoE) is a long-standing challenge. The emergence of the serverless computing paradigm has potential to meet this challenge through its fine-grained management and highly parallel computing structures. However, it is still ambiguous how to implement and configure serverless components to optimize video streaming services. In this paper, we propose EAVS, an Edge-assisted Adaptive Video streaming system with Serverless pipelines, which facilitates fine-grained management for multiple concurrent video transmission pipelines. Then, we design a chunk-level optimization scheme to address video bitrate adaptation. We propose a Deep Reinforcement Learning (DRL) algorithm based on Proximal Policy Optimization (PPO) with a trinal-clip mechanism to make bitrate decisions efficiently for better QoE. Finally, we implement the serverless video streaming system prototype and evaluate the performance of EAVS on various real-world network traces. Our results show that EAVS significantly improves QoE and reduces the video stall rate, achieving over 9.1% QoE improvement and 60.2% latency reduction compared to state-of-the-art solutions.