MU-MIMO Grouping For Real-time Applications

TL;DR

This paper presents MuViS, a reinforcement learning-based MU-MIMO grouping framework that optimizes user grouping and video bitrate to enhance QoE in WiFi networks, addressing practical challenges like heterogeneous conditions and feedback limitations.

Contribution

Introduces MuViS, a dual-phase optimization framework using reinforcement learning for QoE-aware MU-MIMO user grouping and video bitrate adaptation in WiFi networks.

Findings

Supports high video rates with many users in indoor environments.

Scalable framework adaptable to various configurations.

Improves QoE in practical WiFi settings.

Abstract

Over the last decade, the bandwidth expansion and MU-MIMO spectral efficiency have promised to increase data throughput by allowing concurrent communication between one Access Point and multiple users. However, we are still a long way from enjoying such MU-MIMO MAC protocol improvements for bandwidth hungry applications such as video streaming in practical WiFi network settings due to heterogeneous channel conditions and devices, unreliable transmissions, and lack of useful feedback exchange among the lower and upper layers' requirements. This paper introduces MuViS, a novel dual-phase optimization framework that proposes a Quality of Experience (QoE) aware MU-MIMO optimization for multi-user video streaming over IEEE 802.11ac. MuViS first employs reinforcement learning to optimize the MU-MIMO user group and mode selection for users based on their PHY/MAC layer characteristics. The video bitrate is then optimized based on the user's mode (Multi-User (MU) or Single-User (SU)). We present our design and its evaluation on smartphones and laptops using 802.11ac WiFi. Our experimental results in various indoor environments and configurations show a scalable framework that can support a large number of users with streaming at high video rates and satisfying QoE requirements.