timal, as V2X communications links may become congested, which will result in that information not being available in time. It would therefore be beneficial to identify space/time correlations between radio quality data collected by different vehicles in a C-V2X system and a specific vehicle's planned journey 6 Enabling Technologies for better predictability of the quality of the communication along the designated route. Furthermore, how the predicted and journey-specific RNI can be exploited by vehicles to reliably schedule and complete SW package updates and/or content delivery tasks needs more consideration. A possible way to address the above-mentioned challenges would be to design a frame- work based on a MEC infrastructure for cooperative acquisition, partitioning, and distri- bution of information for efficient and journey-specific QoS prediction. The main stages of such a framework can be as follows: 1. Each client application in the vehicle (which is assumed to be under cellular coverage) reports its planned journey information (i.e. map coordinates) to the MEC host (which is also running a geo-location service). The information reported by the client application may also optionally include the version of the installed SW/FW package, the versions of client applications in the vehicle, and other relevant data. 2. Each vehicle provides locally measured radio quality information to the MEC host. The reporting periodicity may vary, depending on the version of its running SW/FW package. Each message containing radio information is tagged with a time stamp and the vehicle's current location. 3. Based on the per-vehicle planned route, the composite information obtained at the MEC host is partitioned by routes as reporting vehicles are traveling. Historical partitioned information can be used to predict QoS characteristics. 4. Assuming the example of SW download, the availability of a new SW package at the net- work side triggers information partitioning by a MEC application. The MEC application takes the journe