用户密集环境下基于边缘智能的直播视频传输优化机制

doi:10.11959/j.issn.1000-436x.2023232

Abstract

Abstract:

The traditional live video transmission optimization mechanism is deployed on the server side, which cannot quickly respond to the dynamic changes of the user’s wireless network environment.To address this problem, a live video transmission optimization mechanism based on edge intelligence called S-Edge was proposed.It was deployed on the OpenWrt-based wireless access point, and comprehensively utilized the wireless channel state information such as airtime utilization and signal-to-noise ratio to make intelligent decisions on terminal priority and transmission rate based on fuzzy logic theory.Furthermore, the active queue management with hierarchical token bucket and service demand-driven wireless transmission rate adaptive control technologies were introduced to realize the real-time scheduling of live video data.In order to verify the effectiveness and performance of the proposed mechanism, a high client-density environment was built through user clusters based on multi-radio interfaces in the real-world scenario.Experimental results show that S-Edge can significantly reduce the average delay and packet loss rate, which meets QoS requirements of live video transmission services in the high client-density environment.

Key words: WLAN, live video transmission, high client-density, edge intelligence

CLC Number:

TP393

Xiaodan GU, Wenjia WU, Zhen LING. Live video transmission optimization mechanism based on edge intelligence in high client-density environment[J]. Journal on Communications, 2023, 44(11): 55-66.

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References 22

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规则	模糊规则条件（IF）					模糊规则结果（THEN）
规则	Th	Ls	Sr	Ns	Au	Pr	MCS index
R₁	L	L	M	M	H	L	M
R₂	L	H	L	L	M	H	L
R₃	M	H	L	L	M	M	L
R₄	H	L	H	H	L	M	H
R₅	H	L	H	H	L	M	M

型号和配置	操作系统	功能
一台x86 PC	Ubuntu 20.04 LTS 64 位	部署在公网的文件下载服务器
一台x86 PC Intel 3168/8265/9260ac无线网卡共18块	Ubuntu 18.04 LTS 64 位	作为基于多射频接口的用户终端集群，用以构建用户密集环境
一台WPQ864开发板（配备QCA9984网卡）	OpenWrt 19.07.8	作为AP，建立和管理Wi-Fi 热点

Live video transmission optimization mechanism based on edge intelligence in high client-density environment

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