Prepare your video for streaming with Segue

TL;DR

Segue enhances video streaming by intelligently chunking videos and augmenting segments based on complexity and adaptation behavior, significantly reducing rebuffering and quality fluctuations.

Contribution

It introduces a novel, adaptation-aware chunking method that considers video complexity and rate adaptation dynamics to improve streaming quality.

Findings

Segue reduces rebuffering and quality fluctuations.

Improves QoE by 9% on average.

Achieves 22% QoE improvement in low-bandwidth conditions.

Abstract

We identify new opportunities in video streaming, involving the joint consideration of offline video chunking and online rate adaptation. Due to a video's complexity varying over time, certain parts are more likely to cause performance impairments during playback with a particular rate adaptation algorithm. To address such an issue, we propose Segue, which carefully uses variable-length video segments, and augment specific segments with additional bitrate tracks. The key novelty of our approach is in making such decisions based on the video's time-varying complexity and the expected rate adaptation behavior over time. We propose and implement several methods for such adaptation-aware chunking. Our results show that Segue substantially reduces rebuffering and quality fluctuations, while maintaining video quality delivered; Segue improves QoE by 9% on average, and by 22% in low-bandwidth conditions. Finally, we view our problem framing as a first step in a new thread on algorithmic and design innovation in video streaming, and leave the reader with several interesting open questions.