Enabling Multicast Transmission for Spatio-Temporally Asynchronous User Requests in Wireless Environments

TL;DR

This paper proposes a new multicast mechanism called set-up based merged multicast (SMMC) that reduces file delivery time in wireless networks by optimizing unicast and multicast phases considering wireless channel uncertainties.

Contribution

It introduces SMMC, a novel multicast approach that jointly optimizes setup and transmission parameters for asynchronous user requests in wireless environments.

Findings

SMMC outperforms traditional unicast methods in delivery time.

Optimizing setup time and transmission rates improves multicast efficiency.

Fine-tuned SMMC further enhances performance using posterior information.

Abstract

The surge in wireless devices and data traffic volume necessitates more efficient transmission methods. Multicasting has garnered consistent attention as a means to fulfill the increasing demand for more efficient data transmission methods. Nevertheless, leveraging multicast wireless networks for spatio-temporally asynchronous data requests poses challenges. In this context, this paper introduces a new multicast mechanism called \emph{set-up based merged multicast (SMMC)} to minimize the delivery time of the requested file in wireless networks by considering the uncertainties inherent in wireless channels. The proposed mechanism comprises two phases. The first phase involves gathering asynchronous requests for a file from users experiencing diverse channel conditions. During this phase, packets of the requested file are transmitted individually in unicast mode within a specified set-up time. Following this, the second phase initiates multicast transmission, which sequentially handles the remaining packets of the file in multicast mode. In the proposed mechanism, we optimize the set-up time and transmission rates of both unicast and multicast modes to minimize the expected file delivery time by jointly taking into account the statistical characteristics of wireless channels, users' locations, and file popularity. Additionally, we also delve into a \emph{fine-tuned SMMC} by utilizing posterior information on the multicast group size and further improve the performance. Our performance evaluations reveal that the proposed SMMC outperforms conventional unicast methods, especially with high-demand data.