A No-Reference Video Streaming QoE Estimator based on Physical Layer 4G Radio Measurements

Abstract

With the increase in consumption of multimedia content through mobile devices (e.g., smartphones), it is crucial to find new ways of optimizing current and future wireless networks and to continuously give users a better Quality of Experience (QoE) when accessing that content. To achieve this goal, it is necessary to provide Mobile Network Operator (MNO) with real time QoE monitoring for multimedia services (e.g., video streaming, web browsing), enabling a fast network optimization and an effective resource management. This paper proposes a new QoE prediction model for video streaming services over 4G networks, using layer 1 (i.e., Physical Layer) key performance indicators (KPIs). The model estimates the service Mean Opinion Score (MOS) based on a Machine Learning (ML) algorithm, and using real MNO drive test (DT) data, where both application layer and layer 1 metrics are available. From the several considered ML algorithms, the Gradient Tree Boosting (GTB) showed the best performance, achieving a Pearson correlation of 78.9%, a Spearman correlation of 66.8% and a Mean Squared Error (MSE) of 0.114, on a test set with 901 examples. Finally, the proposed model was tested with new DT data together with the network's configuration. With the use case results, QoE predictions were analyzed according to the context in which the session was established, the radio transmission environment and radio channel quality indicators.

Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services

“…Concerning the three MOS scores prediction, the results are given in Table 12. We report in this figure the Mean Absolute Error (MAE) [31] and Mean Absolute Percentage Error (MAPE) [32] and Pearson correlation rate (r) [8]. From Table 12, we notice that all the considered models, except the DT method, performed reasonably well on the task of MOS score prediction and showed high degrees of accuracy with at least 81% in the case of buffer-based MOS, 73% in the case of bitratebased MOS and 63% in the case of buffer-based MOS.…”

“…They concluded that the HAS profile is sufficient and better than the radio scenario parameters to estimate user's QoE in the context of LTE technology. Based on the same technology, the authors of [8] introduce a no-Reference video streaming QoE estimator by testing different machine learning techniques. The Gradient Tree Boosting (GTB) method is selected to calculate the video QoE using 11 considered radio parameters.…”

On Machine Learning Based Video QoE Estimation Across Different Networks

“…The performance of QoE/KPI estimation models is greatly affected by playback-related user interactions, which has been demonstrated in [24], stressing the need to include such interactions in the model training phase. The actual utilization of QoE/KPI estimation models in the network has been briefly addressed in [25], [26], [27], but the exact mapping of these models to network architectures and the amount of resources required for their operation is still unclear.…”