This paper presents a focused analysis of enhancing the Internet of Bio-Nano Things (IoBNT) network performance by addressing congestion and improving throughput. We introduce a propagation model for a high-throughput DNA-based track-hopper channel to study congestion using molecular hopper dynamics and Markov state transitions alongside a novel approach for throughput measurement. Our research aims at reducing congestion by increasing information density, allowing for more efficient data transmission with fewer packets. Initially, attempts to amplify density encountered challenges related to bio-compatibility and increased decoding errors. Moreover, congestion precipitated the formation of problematic DNA structures such as hairpins during sequencing. We adopted the yin-yang coding (YYC) to overcome these hurdles, encoding two binary bits into one nucleotide for sequences compatible with synthesis and sequencing technologies. Simulation results validate the YYC coding mechanism’s effectiveness and propagation models’ robustness, significantly advancing IoBNT network performance optimization.