dc.contributor.author | Li, Z | en_US |
dc.contributor.author | Li, J | en_US |
dc.contributor.author | Wu, Q | en_US |
dc.contributor.author | Tyson, G | en_US |
dc.contributor.author | Xie, G | en_US |
dc.date.accessioned | 2023-02-17T14:01:09Z | |
dc.date.issued | 2022-09-20 | en_US |
dc.identifier.issn | 1558-0660 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/84519 | |
dc.description.abstract | Mobile Live Streaming (MLS) services are one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks. Nevertheless, we still lack a deep understanding of MLS user behavior that is critical for optimizing MLS systems, despite some active measurements on viewer-side behavior. Using detailed logs obtained from a major MLS provider, this paper first conducts an in-depth measurement study of both viewer-side and broadcaster-side behavior. Key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized. Inspired by our findings, we propose EDGEOPT– a centralized control center for MLS services for optimizing both the first-mile and the last-mile transmission in MLS. Specifically, EDGEOPT reduces wasteful uploading by 71% through adaptive uploading and enhances the replay quality of popular video segments by 10% via highlights retransmission. EDGEOPT also uses a learning-based content pre-fetching scheme that boosts the viewing startup by 29.5% and offloads at most 80% of the viewing workload from the edge servers with peer-assisted delivery. | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.ispartof | IEEE Transactions on Mobile Computing | en_US |
dc.title | A Large-Scale Measurement and Optimization of Mobile Live Streaming Services | en_US |
dc.type | Article | |
dc.rights.holder | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/TMC.2022.3208094 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |