Modern communication networks generate enormous volumes of data due to cloud computing, multimedia streaming, Internet of Things (IoT) devices, and distributed applications. Efficient utilization of network bandwidth has become increasingly important for maintaining high throughput and low transmission latency. Traditional lossless compression techniques often employ fixed compression strategies without considering changing network conditions, leading to suboptimal performance. This paper presents a Throughput-Aware Compression Framework (TACF) that dynamically adapts compression parameters according to network throughput characteristics. The proposed framework integrates traffic monitoring, statistical analysis, adaptive compression control, and lightweight lossless encoding to optimize data transmission efficiency. Experimental results demonstrate improvements in throughput utilization, compression ratio, and transmission latency when compared with conventional Huffman, LZW, and static Golomb-Rice compression methods. The framework is suitable for modern communication infrastructures including cloud networks, data centers, and IoT environments.
C et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: