  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.
