The computational demands of multimedia data processing are steadily increasing as consumers call for progressively more complex and intelligent multimedia services. New multi-core hardware architectures provide the required resources, but writing parallel, distributed applications remains a labor-intensive task compared to their sequential counter-part. For this reason, Google and Microsoft implemented their respective processing frameworks MapReduce and Dryad, as they allow the developer to think sequentially, yet benefit from parallel and distributed execution. An inherent limitation in the design of these batch processing frameworks is their inability to express arbitrarily complex workloads. The dependency graphs of the frameworks are often limited to directed acyclic graphs, or even pre-determined stages. This is particularly problematic for video encoding and other algorithms that depend on iterative execution. With the Nornir runtime system for parallel programs, which is a Kahn Process Network implementation, we addressed and solved several of these limitations. However, it is more difficult to use than other frameworks due to its complex programming model. In this paper, we build on the knowledge gained from Nornir and present a new framework, called P2G, designed specifically for developing and processing distributed real-time multimedia data. P2G supports arbitrarily complex dependency graphs with cycles, branches and deadlines, and provides both data- and task-parallelism. The framework is implemented to scale transparently with available (heterogeneous) resources, a concept familiar from the cloud computing paradigm. We have implemented an (interchangeable) P2G kernel language to ease development. In this paper, we present a proof of concept implementation of a P2G execution node and some experimental examples using complex workloads like Motion JPEG and K-means clustering. The results show that the P2G system is a feasible approach to multimedia proce- - ssing.