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.

中文翻译：

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MuViS：用于通过商用 WiFi 的多用户应用程序的在线 MU-MIMO 分组

在过去十年中，带宽扩展和 MU-MIMO 频谱效率承诺通过允许一个接入点和多个用户之间的并发通信来提高数据吞吐量。然而，由于异构信道条件和设备、不可靠的传输以及较低层之间缺乏有用的反馈交换，我们距离享受这种 MU-MIMO MAC 协议改进还有很长的路要走，例如在实际 WiFi 网络设置中的视频流等需要带宽的应用。和上层的要求。本文介绍了 MuViS，这是一种新颖的双阶段优化框架，它针对 IEEE 802.11ac 上的多用户视频流提出了体验质量 (QoE) 感知 MU-MIMO 优化。MuViS 首先采用强化学习，根据用户的 PHY/MAC 层特性优化 MU-MIMO 用户组和模式选择。然后根据用户的模式（多用户 (MU) 或单用户 (SU)）优化视频比特率。我们在使用 802.11ac WiFi 的智能手机和笔记本电脑上展示我们的设计及其评估。我们在各种室内环境和配置中的实验结果显示了一个可扩展的框架，可以支持大量用户以高视频速率进行流式传输并满足 QoE 要求。