Inferring Video Streaming Quality of Real-Time Communication Inside Network

被引：0

作者：

Zhang, Yihang ^{[1
]}

Cheng, Sheng ^{[1
]}

Guo, Zongming ^{[1
]}

Zhang, Xinggong ^{[1
]}

机构：

[1] Peking Univ, Wangxuan Inst Comp Technol, Beijing 100871, Peoples R China

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY | 2024年 / 34卷 / 08期

关键词：

Streaming media; Real-time systems; Video recording; Quality assessment; Transformers; Telecommunication traffic; Videoconferences; QoE; generalized transformer; TRANSFORMER; FRAMEWORK;

D O I：

10.1109/TCSVT.2024.3375604

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Real-time video streaming is getting indispensable in people's daily life, and poses heavy loads and stringent performance requirements on the network. For Internet Service Providers (ISPs), ensuring high-quality real-time video communication is a widely concerned issue. However, inferring the quality of real-time video streaming based on passively-collected network traffic is a great challenge due to limited information in the User Datagram Protocol (UDP) header and the encryption of the application-level protocol. In this paper, we propose IReaV-T to Infer Real-time Video streaming quality with a generalized Transformer, which understands the intrinsic state of the network and predicts the future real-time video quality. By applying novel embedding methods, IReaV-T could make full use of observed traffic features and distinguish different real-time video applications. Extensive comparative experiments demonstrate the effectiveness of IReaV-T, showing that IReaV-T could predict future real-time video quality with mean squared Video Multimethod Assessment Fusion (VMAF) score error less than 6.

引用

页码：7756 / 7770

页数：15

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