Keywords: Video streaming, video encoding, bitrate adaptation
TL;DR: Segue is a video delivery system that, by fine tuning the video segments length and quality levels, improves the experience of streaming by taking into account the playback context dependence.
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.
Conflicts: All (ETH), Anja Feldmann (MPI), Brighten Godfrey (UIUC), Balakrishnan Chandrasekaran (MPI), Bruce Maggs (Duke), Ilker Nadi Bozkurt (Google), Sangeetha Abdu Jyothi (UC Irvine), Michael Schapira (HUJI), Eitan Zahavi (Mellanox), Dan Alistarh (IST Austria), Ji Liu (Kwai), Chrisots Gkantsidis (Microsoft), Thomas Karagiannis (Microsoft), Francesca Parmigiani (Microsoft), Mark Filer (Microsoft), Jeff Cox (Microsoft), Anna Ptasznik (Microsoft), Nick Harland (Microsoft), Winston Saunders (Microsoft), Christian Belady (Microsoft)
Potential Reviewers: Romain Jacob, Junchen Jiang