As broadcast systems continue their transition from dedicated hardware appliances to standard commercial off-the-shelf (COTS) computing platforms, the efficiency of data movement becomes a critical factor in overall system performance. In particular, the growing reliance on GPU-accelerated processing for real-time media encoding, transcoding, and image manipulation demands optimized data exchange both within compute nodes and across distributed systems. This shift introduces new challenges in memory sharing and communication, especially when dealing with heterogeneous memory hierarchies and high-throughput, low-latency requirements. This study presents a comparative evaluation of memory sharing mechanisms across three distinct GPU communication paths: host-to-GPU transfers, intra-node GPU-to-GPU communication, and inter-node GPU memory exchange. This study highlights how higher-level communication frameworks provide flexible abstraction that simplifies development across heterogeneous platforms, while enabling portability and scalability.