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While the rollout of the fifth-generation mobile network (5G) is underwayacross the globe with the intention to deliver 4K/8K UHD videos, AugmentedReality (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 arebeing deployed. This called for a high-performance adaptive bitrate (ABR)algorithm that can maximize the user quality of experience given 5G networkcharacteristics and data rate of UHD contents.  Recently, many of the newly proposed ABR techniques were machine-learningbased. Among that, Pensieve is one of the state-of-the-art techniques, whichutilized reinforcement-learning to generate an ABR algorithm based onobservation of past decision performance. By incorporating the context of the5G network and UHD content, Pensieve has been optimized into Pensieve 5G. NewQoE metrics that more accurately represent the QoE of UHD video streaming onthe different types of devices were proposed and used to evaluate Pensieve 5Gagainst other ABR techniques including the original Pensieve. The results fromthe simulation based on the real 5G Standalone (SA) network throughput showsthat Pensieve 5G outperforms both conventional algorithms and Pensieve with theaverage QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve5G 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 5GStandalone (SA) network.

Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network