AI

CQIC: Revisiting Cross-Layer Congestion Control f or Cellular Networks

Abstract

With the advent of high-speed cellular access and the overwhelming popularity of smartphones, a large percent of today’s Internet content is being delivered via cellular links. Due to the nature of long-range wireless signal propagation, the capacity of the last hop cellular link can vary by orders of magnitude within a short period of time (e.g., a few seconds). Unfortunately, TCP does not perform well in such fast-changing environments, potentially leading to poor spectrum utilization and high end-to-end packet delay.

In this paper we revisit seminal work in cross-layer optimization the context of 4G cellular networks. Specifically, we leverage the rich physical layer information exchanged between base stations (NodeB) and mobile phones (UE) to predict the capacity of the underlying cellular link, and propose CQIC, a cross-layer congestion control design. Experiments on real cellular networks confirm that our capacity estimation method is both accurate and precise. A CQIC sender uses these capacity estimates to adjust its packet sending behavior. Our preliminary evaluation reveals that CQIC improves throughput over TCP by 1.08–2.89 × for small and medium flows. For large flows, CQIC attains throughput comparable to TCP while reducing the average RTT by 2.38–2.65x.