Inferring Video Streaming Quality of Real-Time Communication Inside Network

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.