URL: https://doi.org/10.1145/3341302.3342064
Bibtex Entry:
@inproceedings{2019-Ray-sigcomm, author = “Ray, Devdeep and Kosaian, Jack and Rashmi, K. V. and Seshan, Srinivasan”, title = “Vantage: Optimizing Video Upload for Time-Shifted Viewing of Social Live Streams”, year = “2019”, month = “August”, isbn = “9781450359566”, publisher = “Association for Computing Machinery”, address = “New York, NY, USA”, url = “https://doi.org/10.1145/3341302.3342064”, doi = “10.1145/3341302.3342064”, abstract = “Social live video streaming (SLVS) applications are becoming increasingly popular with the rise of platforms such as Facebook-Live, YouTube-Live, Twitch and Periscope. A key characteristic that differentiates this new class of applications from traditional live streaming is that these live streams are watched by viewers at different delays; while some viewers watch a live stream in real-time, others view the content in a time-shifted manner at different delays. In the presence of variability in the upload bandwidth, which is typical in mobile environments, existing solutions silo viewers into either receiving low latency video at a lower quality or a higher quality video with a significant delay penalty, without accounting for the presence of diverse time-shifted viewers.In this paper, we present Vantage, a live-streaming upload solution that improves the overall quality of experience for diverse time-shifted viewers by using selective quality-enhancing retransmissions in addition to real-time frames, optimizing the encoding schedules to balance the allocation of the available bandwidth between the two. Our evaluation using real-world mobile network traces shows that Vantage can provide high quality simultaneously for both low-latency and delayed viewing.For delayed viewing, Vantage achieves an average improvement of 19.9\% over real-time optimized video streaming techniques across all the network traces and test videos, with observed gains of up to 42.9\%. These benefits come at the cost of an average drop in real-time quality of 3.3\%, with a maximum drop of 7.1\%. This represents a significant performance improvement over current techniques used for SLVS applications, which primarily optimize the video upload for real-time viewing.”, booktitle = “ACM SIGCOMM”, pages = “380–393”, numpages = “14”, keywords = “VOD, adaptive bitrate algorithms, live streaming, video delivery”, location = “Beijing, China”, series = “SIGCOMM ‘19” }