The measurement of the Quality of Experience (QoE) of multimedia streaming services (MMSS) over IP networks may be realized thanks to objective QoE models. They are mathematical functions transforming metrics from technical to user domains. An interesting category of QoE models predicts QoE scores of MMSS at runtime, letting use them for the monitoring operation. This requires integrating them inside the production environments, i.e., where the actual MMSS are consumed by end-users. This aspect is often neglected by QoE modelers that focus mainly on the accuracy and fitness of their designed models with respect to a given set of settings and conditions. As a consequence, a considerable technical effort should be made in order to bring them from the laboratory to the production environments. This obviously discourages MMSS providers to easily accept and adopt them. For the sake of enhancing QoE models integration, we propose Mesukal, a software-layer ensuring portability of QoE models over a variety of underlying MMSS, e.g. YouTube or Netflix. Specifically, Mesukal acts as a Java Virtual Machine (JVM) enabling to build portable applications over different OS, e.g. Windows, Linux or MacOS. Mesukal can be considered as a virtual MMSS that is able to seamlessly interact with QoE models, on the one hand, and arbitrary real MMSS, on the other hand. Each considered MMSS over IP networks is appropriately virtualized by a dedicated Mesukal App. Besides real MMSS, Mesukal can be used to instantiate experimental MMSS where the accuracy and portability of QoE models may be inspected and checked under controlled conditions. The inputs needed by the concerned QoE models are fetched from each real MMSS using probes that are tailored following the technology used by the considered multimedia service. In addition, Mesukal includes a rich GUI dashboard that enables to inspect QoE results.