VCCAnalyzer: Identifying Congestion Control Algorithms used by Video Streaming Services

CCAnalyzer is a recent proposal for a congestion control algorithm (CCA) classifier to measure the deployment of congestion control algorithms in the Internet today by creating a controlled bottleneck and monitoring bottleneck queue occupancy between third-party websites and a controlled receiver. However, this work was designed for web traffic, whereas most internet traffic today is video streaming. Video streaming applications pose additional challenges in CCA classification due to the interactions between adaptive bitrate (ABR) algorithms and CCAs. In this work, we present VCCAnalyzer, which addresses these challenges by carefully selecting bottleneck bandwidth rates to minimize ABR interference and applying interpolation and smoothing techniques to create classifiable queue occupancy traces. VCCAnalyzer achieves 100% accuracy, higher accuracy than other approaches in video classification. To demonstrate the efficacy of VCCAnalyzer, we conduct a measurement study examining the CCA deployment across major streaming platforms, including Disney+, Hulu, Twitch, and other commonly visited websites. Our findings reveal some variations in congestion control strategies between these services. Our results demonstrate that VCCAnalyzer can effectively classify CCAs in video environments.