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.

翻译：蜂窝网络上的自适应比特率（ABR）流传输已在文献中得到充分研究；然而，现有ABR算法主要聚焦于提升终端用户的体验质量（QoE），却忽略了底层设备的资源消耗。因此，为维持用户QoE而主动下载视频数据的行为，除非下载尝试与网络信道条件同步，否则常会缩短设备电池寿命。本研究开发了EnDASH-5G——一种基于流行DASH的ABR流传输算法的包装器，通过利用网络感知的视频数据下载机制实现这种同步。EnDASH-5G采用新颖的5G毫米波网络吞吐量预测机制，借助基于迁移学习的方法并利用公开5G数据集，对现有吞吐量预测模型进行升级。随后，它利用深度强化学习，根据预测吞吐量动态决定播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，从而节省设备电池电量。通过对EnDASH-5G的全面评估，我们观察到，与最先进的Pensieve ABR算法相比，其最大能耗降低了近30.5%，同时在QoE方面表现几乎持平。