PPO-ABR: Proximal Policy Optimization based Deep Reinforcement Learning for Adaptive BitRate streaming
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Providing a high Quality of Experience (QoE) for video streaming in 5G and beyond 5G (B5G) networks is challenging due to the dynamic nature of the underlying network conditions. Several Adaptive Bit Rate (ABR) algorithms have been developed to improve QoE, but most of them are designed based on fixed rules and unsuitable for a wide range of network conditions. Recently, Deep Reinforcement Learning (DRL) based Asynchronous Advantage Actor-Critic (A3C) methods have recently demonstrated promise in their ability to generalise to diverse network conditions, but they still have limitations. One specific issue with A3C methods is the lag between each actor's behavior policy and central learner's target policy. Consequently, suboptimal updates emerge when the behavior and target policies become out of synchronization. In this paper, we address the problems faced by vanilla-A3C by integrating the on-policy-based multi-agent DRL method into the existing video streaming framework. Specifically, we propose a novel system for ABR generation - Proximal Policy Optimization-based DRL for Adaptive Bit Rate streaming (PPO-ABR). Our proposed method improves the overall video QoE by maximizing sample efficiency using a clipped probability ratio between the new and the old policies on multiple epochs of minibatch updates. The experiments on real network traces demonstrate that PPO-ABR outperforms state-of-the-art methods for different QoE variants.
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