This paper presents a QoS-aware, content-aware and device-aware nonintrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the acceptable diagnostic quality through a device-aware adaptive video streaming mechanism. The proposed model is trained for an unseen dataset of input variables such as QoS, content features and display

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... device characteristics, to produce an output value in the form of m-QoE (i.e. MOS). The efficiency of the proposed model is validated through subjective tests carried by medical experts. The prediction accuracy obtained via the correlation coefficient and Root Mean-Square-Error (RMSE) indicates that the proposed model succeeds in measuring m-QoE closer to the visual perception of the medical experts. Furthermore, we have addressed two main research questions: (1) How significant is ultrasound video content type in determining m-QoE? (2) How much of a role does the screen size and device resolution play in medical experts' diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatiotemporal features, by including these features in the proposed prediction model, and validating their significance through medical experts' subjective ratings. The latter is answered by conducting a novel subjective experiment of the ultrasound video sequences across multiple devices.