GreenABR: energy-aware adaptive bitrate streaming with deep reinforcement learning
GreenABR: energy-aware adaptive bitrate streaming with deep reinforcement learning
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DOI：
10.1145/3524273.3528188
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发表时间：
2022-06
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通讯作者：
B. Turkkan;Ting Dai;Adithya Raman;T. Kosar;Changyou Chen;Muhammed Fatih Bulut;J. Zola;Daby M. Sow
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作者：
B. Turkkan;Ting Dai;Adithya Raman;T. Kosar;Changyou Chen;Muhammed Fatih Bulut;J. Zola;Daby M. Sow
自适应比特率 (ABR) 算法旨在在动态变化的网络条件下做出最佳比特率决策，以确保用户在视频流传输期间获得高质量的体验 (QoE)。然而，大多数现有的 ABR 都存在预定义规则的局限性以及关于流参数的错误假设。他们还没有考虑 QoE 模型中的感知质量，无论如何都以更高的比特率为目标，并忽略了相应的能耗。这种联合方法会导致额外的能源消耗并成为负担，特别是对于移动设备用户而言。本文提出了 GreenABR，一种基于深度强化学习的新型 ABR 方案，可在不牺牲用户 QoE 的情况下优化视频流期间的能耗。 GreenABR 采用标准感知质量指标、VMAF 和通过流应用程序收集的有功功率测量值。 GreenABR的深度强化学习模型不对流环境做出任何假设，并学习如何适应各种真实网络场景中动态变化的条件。 GreenABR 优于现有最先进的 ABR 算法，可节省高达 57% 的流媒体能耗和 60% 的数据消耗，同时由于重新缓冲时间减少高达 84% 且容量违规接近于零，感知 QoE 提高高达 22%。
Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations.