  While the rollout of the fifth-generation mobile network (5G) is underway
across the globe with the intention to deliver 4K/8K UHD videos, Augmented
Reality (AR), and Virtual Reality (VR) content to the mass amounts of users,
the coverage and throughput are still one of the most significant issues,
especially in the rural areas, where only 5G in the low-frequency band are
being deployed. This called for a high-performance adaptive bitrate (ABR)
algorithm that can maximize the user quality of experience given 5G network
characteristics and data rate of UHD contents.
  Recently, many of the newly proposed ABR techniques were machine-learning
based. Among that, Pensieve is one of the state-of-the-art techniques, which
utilized reinforcement-learning to generate an ABR algorithm based on
observation of past decision performance. By incorporating the context of the
5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New
QoE metrics that more accurately represent the QoE of UHD video streaming on
the different types of devices were proposed and used to evaluate Pensieve 5G
against other ABR techniques including the original Pensieve. The results from
the simulation based on the real 5G Standalone (SA) network throughput shows
that Pensieve 5G outperforms both conventional algorithms and Pensieve with the
average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve
5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC)
Network, despite the training being done solely using the data from the 5G
Standalone (SA) network.
