A buffer-based approach to rate adaptation

TLDR

Existing ABR algorithms struggle to estimate future capacity because capacity varies widely over time, a common phenomenon in commercial services. This study proposes using only the buffer to determine video rate and invoking capacity estimation only when necessary, rather than assuming it is always required. The authors evaluate a buffer‑based rate adaptation strategy through large‑scale experiments on millions of users, beginning with a simple design that selects video rate solely from current buffer occupancy. The buffer‑based approach eliminates the need for capacity estimation in steady state, but benefits from simple past‑throughput estimation during startup, reducing rebuffering by 10‑20% versus Netflix’s default ABR while maintaining similar average video rates and achieving higher rates in steady state.

Abstract

Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.