arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2302.06004cs.NI
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通过5G网络感知视频流提高UE能效

Improving UE Energy Efficiency through Network-aware Video Streaming over 5G

Basabdatta Palit, Argha Sen, Abhijit Mondal, Ayan Zunaid, Jay Jayatheerthan, Sandip Chakraborty

摘要:

蜂窝网络上的自适应比特率(ABR)流传输在文献中已经得到了很好的研究;然而,现有的ABR算法主要关注于提高终端用户的体验质量(QoE),而忽略了底层设备的资源消耗方面。因此,主动尝试下载视频数据以维持用户的QoE通常会影响底层设备的电池寿命,除非下载尝试与网络的信道条件同步。在这项工作中,我们开发了EnDASH-5G -一个包装在流行的基于DASH的ABR流算法,它建立了这种同步,利用网络感知的视频数据下载机制。EnDASH-5G通过利用公共可用的5G数据集的基于迁移学习的方法升级现有的吞吐量预测模型,为5G毫米波网络利用了一种新的吞吐量预测机制。然后,它利用深度强化学习来使用预测的吞吐量动态地决定回放缓冲区长度和视频比特率。这确保了数据下载尝试与底层网络条件同步,从而节省了设备的电池电量。从对EnDASH-5G的全面评估中,我们观察到它在最大能耗方面比最先进的Pensieve ABR算法实现了接近$30.5\%$的降低,同时在QoE方面几乎达到了标准。

英文摘要:

Adaptive Bitrate (ABR) Streaming over the cellular networks has been well studied in the literature; however, existing ABR algorithms primarily focus on improving the end-users' Quality of Experience (QoE) while ignoring the resource consumption aspect of the underlying device. Consequently, proactive attempts to download video data to maintain the user's QoE often impact the battery life of the underlying device unless the download attempts are synchronized with the network's channel condition. In this work, we develop EnDASH-5G -- a wrapper over the popular DASH-based ABR streaming algorithm, which establishes this synchronization by utilizing a network-aware video data download mechanism. EnDASH-5G utilizes a novel throughput prediction mechanism for 5G mmWave networks by upgrading the existing throughput prediction models with a transfer learning-based approach leveraging publicly available 5G datasets. It then exploits deep reinforcement learning to dynamically decide the playback buffer length and the video bitrate using the predicted throughput. This ensures that the data download attempts get synchronized with the underlying network condition, thus saving the device's battery power. From a thorough evaluation of EnDASH-5G, we observe that it achieves a near $30.5\%$ decrease in the maximum energy consumption than the state-of-the-art Pensieve ABR algorithm while performing almost at par in QoE.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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