QUETRA: A queuing theory approach to DASH rate adaptation

Abstract

DASH, or Dynamic Adaptive Streaming over HTTP, relies on a rate adaptation component to decide on which representation to download for each video segment. A plethora of rate adaptation algorithms has been proposed in recent years. The decisions of which bitrate to download made by these algorithms largely depend on several factors: estimated network throughput, buffer occupancy, and buffer capacity. Yet, these algorithms are not informed by a fundamental relationship between these factors and the chosen bitrate, and as a result, we found that they do not perform consistently in all scenarios, and require parameter tuning to work well under different buffer capacity. In this paper, we model a DASH client as an M/D/l/K queue, which allows us to calculate the expected buffer occupancy given a bitrate choice, network throughput, and buffer capacity. Using this model, we propose QUETRA, a simple rate adaptation algorithm. We evaluated QUETRA under a diverse set of scenarios and found that, despite its simplicity, it leads to better quality of experience (7% - 140%) than existing algorithms. © 2017 Association for Computing Machinery.