  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.
