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Hard real-time (HRT) video systems require admission control decisions that rely on two factors. Firstly, schedulability analysis of the data-dependent, communicating tasks within the application need to be carried out in order to guarantee timing and predictability. Secondly, the allocation of the tasks to multi-core processing elements would generate different results in the schedulability analysis. Due to the conservative nature of the state-of-the-art schedulability analysis of tasks and message flows, and the unpredictability in the application, the system resources are often under-utilised. In this paper we propose two blocking-aware dynamic task allocation techniques that exploit application and platform characteristics, in order to increase the number of simultaneous, fully schedulable, video streams handled by the system. A novel, worst-case response time aware, search-based, static hard real-time task mapper is introduced to act as an upper-baseline to the proposed techniques. Further evaluations are carried out against existing heuristic-based dynamic mappers. Improvements to the admission rates and the system utilisation under a range of different workloads and platform sizes are explored.