The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.