Paper accepted for 20th International Conference on Network and Service Management (CNSM 2024)

Title: High Complexity and Bad Quality? Efficiency Assessment for Video QoE Prediction Approaches

Authors: Frank Loh, Gülnaziye Bingöl, Reza Farahani, Andrea Pimpinella, Radu Prodan, Luigi Atzori, Tobias Hoßfeld

Venue: 20th International Conference on Network and Service Management (CNSM 2024)

Abstract:  In recent years, video streaming has dominated Internet data traffic, prompting network providers to ensure high-quality streaming experiences to prevent customer churn. However, due to the encryption of streaming traffic, extensive network monitoring by providers is required to predict the streaming quality and improve their services. Several such prediction approaches have been studied in recent years, with a primary focus on the ability to determine key video quality degradation factors, often without considering the required resources or
energy consumption. To address this gap, we consider existing methods to predict key Quality of Experience (QoE) degradation factors from the literature and quantify the data that have to be monitored and processed for video streaming applications. Based on this, we assess the efficiency of different QoE degradation factor prediction approaches and quantify the ratio between efficiency and the achieved prediction quality. In this context, we identify significant disparities in the efficiency, influenced by data requirements and the specific prediction approach, and finally by the resulting quality. Consequently, we provide insights for network providers to choose the most appropriate method tailored to their specific requirements.