On Throughput-Smoothness Trade-offs in Streaming Communication

TL;DR

This paper investigates the trade-off between throughput and smoothness in streaming communication, analyzing feedback frequency effects and proposing coding schemes for point-to-point and multicast scenarios.

Contribution

It introduces a novel analysis framework using renewal processes and Markov chains, and proposes a spectrum of coding schemes for different trade-offs.

Findings

Frequent feedback improves throughput-smoothness trade-off.

Proposed coding schemes adapt to delay sensitivity and bandwidth.

Analysis framework offers new insights into streaming communication.

Abstract

Unlike traditional file transfer where only total delay matters, streaming applications impose delay constraints on each packet and require them to be in order. To achieve fast in-order packet decoding, we have to compromise on the throughput. We study this trade-off between throughput and smoothness in packet decoding. We first consider a point-to-point streaming and analyze how the trade-off is affected by the frequency of block-wise feedback, whereby the source receives full channel state feedback at periodic intervals. We show that frequent feedback can drastically improve the throughput-smoothness trade-off. Then we consider the problem of multicasting a packet stream to two users. For both point-to-point and multicast streaming, we propose a spectrum of coding schemes that span different throughput-smoothness tradeoffs. One can choose an appropriate coding scheme from these, depending upon the delay-sensitivity and bandwidth limitations of the application. This work introduces a novel style of analysis using renewal processes and Markov chains to analyze coding schemes.