Detecting service quality issues of adaptive streaming video traffic in mobile networks – a practical approach

With the rapid growth of video streaming services, particularly under the advent of 5G, ensuring optimal enduser service quality has become a pressing challenge for mobile network operators (MNOs). The increasing demand for high-bandwidth, low-latency video applications, such as live streaming and ultra-high-definition content, requires new approaches to detect and mitigate service quality degradation. This paper investigates the use of machine learning models for estimating end-user service quality in 5G networks for adaptive video streaming performance. We analyze real-world data collected from a 5G standalone network, combining in-network metrics, client-side data, and radio environment measures. A key aspect of this study is comparing the effectiveness of the currently available mobile core network data from the User Plane Function with high-resolution (1-2 second) client-side data. The client-side measurements were obtained by the GoDASH framework and was used for proposing improvements on core network user plane probing function. Although client side measured service quality measurement are the ideal, our results demonstrate that core network transport probing data can be used for estimating the end user service quality or detecting video stall but the probing metrics should be synchronized with the video chunks. Including radio environment data in the input dataset of the model only slightly improves the performance. Higher resolution core network data improves the model accuracy but they would be needed for covering mobility scenarios and changing traffic conditions. This research provides actionable insights for MNOs to enhance network analytics and address potential service quality issues in 5G environments.