An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP

TLDR

Adaptive video streaming over HTTP is increasingly adopted because it improves user quality and optimizes resource use for providers. The study experimentally evaluates the rate‑adaptation mechanisms of Smooth Streaming, Netflix, and OSMF under bandwidth fluctuations, player competition, and live‑content scenarios to assess convergence, fairness, and playback delay. Experiments were conducted on the three players across three operating conditions—persistent or short‑term bandwidth changes, shared bottleneck competition, and live streaming—to observe adaptation behavior. The results reveal substantial differences and notable inefficiencies among the three players.

Abstract

Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.