Device-Based Cellular Throughput Prediction for Video Streaming: Lessons From a Real-World Evaluation

Abstract

AI-driven data analysis methods have garnered attention in enhancing the performance of wireless networks. One such application is the prediction of downlink throughput in mobile cellular networks. Accurate throughput predictions have demonstrated significant application benefits, such as improving the quality of experience in adaptive video streaming. However, the high degree of variability in cellular link behaviour, coupled with device mobility and diverse traffic demands, presents a complex problem. Numerous published studies have explored the application of machine learning to address this problem, displaying potential when trained and evaluated with traffic traces collected from operational networks. The focus of this paper is an empirical investigation of machine learning-based throughput prediction that runs in real-time on a smartphone, and its evaluation with video streaming in a range of real-world cellular network settings. We report on a number of key challenges that arise when performing prediction “in the wild”, dealing with practical issues one encounters with online data (not traces) and the limitations of real smartphones. These include data sampling, distribution shift, and data labelling. We describe our current solutions to these issues and quantify their efficacy, drawing lessons that we believe will be valuable to network practitioners planning to use such methodologies in operational cellular networks.