Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.

(2024). High Complexity and Bad Quality? Efficiency Assessment for Video QoE Prediction Approaches . Retrieved from https://hdl.handle.net/10446/294465

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

Pimpinella, Andrea;
2024-01-01

Abstract

Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.
2024
Loh, Frank; Bingöl, Gülnaziye; Farahani, Reza; Pimpinella, Andrea; Prodan, Radu; Atzori, Luigi; Hoßfeld, Tobias
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/294465
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