Prepare your video for streaming with Segue

We identify new opportunities in video streaming, involving the joint consideration of offline video chunking and online rate adaptation. We observe that 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 this, we propose careful use of variable-length video segments, and augmentation of certain segments with additional bitrate tracks. The key novelty of Segue is in making these 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. Beyond our specific approach, 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.