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基于WebRTC的低延迟流媒体传输方法研究

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基于WebRTC的低延迟流媒体传输方法研究针对实时音视频通信中的延迟问题,通过集成深度强化学习(Deep Reinforcement Learning,DRL)优化视频速率控制,并与标准Google拥塞控制算法(Google Conges-tion Control,GCC)比较.研究在简单、中等和困难网络状态下评估模型性能,结果表明DRL模型在丢包率和往返时延上均优于GCC,尤其在困难模式下展现出更强的适应性和鲁棒性,为低延迟流媒体传输提供了有效解决方案.
Research on Low Delay Streaming Media Transmission Method Based on WebRTC
Research on low-latency streaming media transmission method based on WebRTC Aiming at the delay problem in real-time audio and video communication,the video rate control is optimized by integrating Deep Reinforcement Learning(DRL)and compared with the standard Google Congestion Control algorithm(GCC).The performance of the model is evaluated in simple,medium and dif-ficult network conditions.The results show that DRL model is superior to GCC in packet loss rate and round-trip delay,especially in diffi-cult mode,which provides an effective solution for low-delay streaming media transmission.

WebRTCLow delayStreaming media

陈震、卢金勤、朱奇、朱磊

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江西锦路科技开发有限公司,江西南昌 330025

WebRTC 低延迟 流媒体

2024

机电产品开发与创新
中国机械工业联合会

机电产品开发与创新

影响因子:0.211
ISSN:1002-6673
年,卷(期):2024.37(6)