Adaptive Cooperative Streaming of Holographic Video Over Wireless Networks: A Proximal Policy Optimization Solution

TL;DR

This paper introduces a reinforcement learning-based framework for adaptive cooperative holographic video streaming over wireless networks, optimizing quality and stability amidst channel fluctuations.

Contribution

It proposes a novel QoE metric for holographic video and applies PPO to jointly optimize beamforming and bitrate control in a cooperative wireless streaming setting.

Findings

Proposed scheme outperforms baseline methods in simulations.

Effective adaptation to wireless channel fluctuations.

Enhanced holographic video quality and stability.

Abstract

Adapting holographic video streaming to fluctuating wireless channels is essential to maintain consistent and satisfactory Quality of Experience (QoE) for users, which, however, is a challenging task due to the dynamic and uncertain characteristics of wireless networks. To address this issue, we propose a holographic video cooperative streaming framework designed for a generic wireless network in which multiple access points can cooperatively transmit video with different bitrates to multiple users. Additionally, we model a novel QoE metric tailored specifically for holographic video streaming, which can effectively encapsulate the nuances of holographic video quality, quality fluctuations, and rebuffering occurrences simultaneously. Furthermore, we formulate a formidable QoE maximization problem, which is a non-convex mixed integer nonlinear programming problem. Using proximal policy optimization (PPO), a new class of reinforcement learning algorithms, we devise a joint beamforming and bitrate control scheme, which can be wisely adapted to fluctuations in the wireless channel. The numerical results demonstrate the superiority of the proposed scheme over representative baselines.