Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network
Kasidis Arunruangsirilert, Bo Wei, Hang Song, Jiro Katto

TL;DR
This paper introduces Pensieve 5G, a reinforcement learning-based adaptive bitrate algorithm optimized for UHD content delivery over commercial 5G networks, demonstrating significant QoE improvements in simulations.
Contribution
It extends the Pensieve RL-based ABR algorithm to 5G networks with new QoE metrics and validates its effectiveness on real 5G SA and NR-DC networks.
Findings
Pensieve 5G outperforms traditional ABR algorithms.
Achieves 8.8% average QoE improvement over original Pensieve.
Performs well on NR-DC networks despite training on SA data.
Abstract
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…
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Taxonomy
TopicsImage and Video Quality Assessment · Telecommunications and Broadcasting Technologies · Video Coding and Compression Technologies
