2021/06/29 by Hannaneh Barahouei Pasandi, Pasandi, Hannaneh Barahouei, Tamer Nadeem +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimedia (cs.MM) #Networking and Internet Architecture (cs.NI) #Wireless Networks and Protocols #cs.LG #cs.MM #cs.NI
paper · pdf · doi:10.48550/arxiv.2106.15262
openalex publication_date 2021/06/29 · arxiv created 2021/07/26 · arxiv updated 2021/07/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.