Fog-Aided Wireless Networks for Content Delivery: Fundamental Latency Trade-Offs

TL;DR

This paper investigates the fundamental latency limits in fog-aided wireless networks with edge caching and cloud processing, proposing strategies to minimize delivery time and characterizing optimal performance bounds.

Contribution

It introduces an information-theoretic framework for analyzing latency trade-offs, proposing caching and transmission strategies, and deriving bounds on the normalized delivery time in such networks.

Findings

Proposed caching and transmission strategies reduce latency.

Derived lower bounds on normalized delivery time.

Achieved near-optimal performance within a factor of 2.

Abstract

A fog-aided wireless network architecture is studied in which edge-nodes (ENs), such as base stations, are connected to a cloud processor via dedicated fronthaul links, while also being endowed with caches. Cloud processing enables the centralized implementation of cooperative transmission strategies at the ENs, albeit at the cost of an increased latency due to fronthaul transfer. In contrast, the proactive caching of popular content at the ENs allows for the low-latency delivery of the cached files, but with generally limited opportunities for cooperative transmission among the ENs. The interplay between cloud processing and edge caching is addressed from an information-theoretic viewpoint by investigating the fundamental limits of a high Signal-to-Noise-Ratio (SNR) metric, termed normalized delivery time (NDT), which captures the worst-case coding latency for delivering any requested content to the users. The NDT is defined under the assumptions of either serial or pipelined fronthaul-edge transmission, and is studied as a function of fronthaul and cache capacity constraints. Placement and delivery strategies across both fronthaul and wireless, or edge, segments are proposed with the aim of minimizing the NDT. Information-theoretic lower bounds on the NDT are also derived. Achievability arguments and lower bounds are leveraged to characterize the minimal NDT in a number of important special cases, including systems with no caching capabilities, as well as to prove that the proposed schemes achieve optimality within a constant multiplicative factor of 2 for all values of the problem parameters.