  Over the recent years, research and development in adaptive bitrate (ABR)
algorithms for live video streaming have been successful in improving users'
quality of experience (QoE) by reducing latency to near real-time levels while
delivering higher bitrate videos with minimal rebuffering time. However, the
QoE models used by these ABR algorithms do not take into account that a large
portion of live video streaming clients use mobile devices where a higher
bitrate does not necessarily translate into higher perceived quality. Ignoring
perceived quality results in playing videos at higher bitrates without a
significant increase in perceptual video quality and becomes a burden for
battery-constrained mobile devices due to higher energy consumption. In this
paper, we propose LL-GABR, a deep reinforcement learning approach that models
the QoE using perceived video quality instead of bitrate and uses energy
consumption along with other metrics like latency, rebuffering events, and
smoothness. LL-GABR makes no assumptions about the underlying video,
environment, or network settings and can operate flexibly on different video
titles, each having a different bitrate encoding ladder without additional
re-training, unlike existing learning-based ABRs. Trace-driven experimental
results show that LL-GABR outperforms the state-of-the-art approaches by up to
44% in terms of perceptual QoE and a 73% increase in energy efficiency as a
result of reducing net energy consumption by 11%.
