ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming

TL;DR

This paper introduces the ANT model to accurately characterize network throughput dynamics, enabling more effective ABR decisions in adaptive video streaming, significantly improving user QoE across diverse network scenarios.

Contribution

The paper proposes a novel ANT model that captures full spectrum network throughput dynamics and integrates it with RL-based ABR algorithms for enhanced performance.

Findings

QoE improved by 65.5% over Pensive

QoE improved by 31.3% over Oboe

Effective across diverse network types

Abstract

Adaptive Bit Rate (ABR) decision plays a crucial role for ensuring satisfactory Quality of Experience (QoE) in video streaming applications, in which past network statistics are mainly leveraged for future network bandwidth prediction. However, most algorithms, either rules-based or learning-driven approaches, feed throughput traces or classified traces based on traditional statistics (i.e., mean/standard deviation) to drive ABR decision, leading to compromised performances in specific scenarios. Given the diverse network connections (e.g., WiFi, cellular and wired link) from time to time, this paper thus proposes to learn the ANT (a.k.a., Accurate Network Throughput) model to characterize the full spectrum of network throughput dynamics in the past for deriving the proper network condition associated with a specific cluster of network throughput segments (NTS). Each cluster of NTS is then used to generate a dedicated ABR model, by which we wish to better capture the network dynamics for diverse connections. We have integrated the ANT model with existing reinforcement learning (RL)-based ABR decision engine, where different ABR models are applied to respond to the accurate network sensing for better rate decision. Extensive experiment results show that our approach can significantly improve the user QoE by 65.5% and 31.3% respectively, compared with the state-of-the-art Pensive and Oboe, across a wide range of network scenarios.