Queue-Stable Dynamic Compression and Transmission with Mobile Edge Computing.

With mobile edge computing (MEC), the data compression at the edge devices can effectively improve the communication efficiency by transmitting the compressed data. In this paper, we construct a joint data compression and transmission scheduling framework to optimize the system throughput with the limited transmission resources. Different to most of the existing works, we consider the interaction between the data compression and data transmission to achieve the optimal throughput. Specifically, to explore the effect of data compression, we construct a queue system through constructing the mapping between the original data queues and the compressed data queues under different compression schemes (including the uncompressed queues). We design the transmission scheduling algorithm based on Lyapunov optimization according to the original data queues. Due to the nature that the data compression does not change the original data queue length directly, we choose the optimal data compression scheme considering the achieved utilities when the compressed data are transmitted, which can be estimated via Q-learning. In addition, we theoretically prove the queue stability under our proposed joint data compression and transmission scheduling algorithm. The simulation results show that the proposed algorithm has better delay performance than the conventional schemes.