DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yeo, Hyunho | ko |
dc.contributor.author | Lim, Hwijoon | ko |
dc.contributor.author | Kim, Jae Hong | ko |
dc.contributor.author | Jung, Youngmok | ko |
dc.contributor.author | Ye, Juncheol | ko |
dc.contributor.author | Han, Dongsu | ko |
dc.date.accessioned | 2022-11-24T11:01:59Z | - |
dc.date.available | 2022-11-24T11:01:59Z | - |
dc.date.created | 2022-11-18 | - |
dc.date.created | 2022-11-18 | - |
dc.date.issued | 2022-08-26 | - |
dc.identifier.citation | 2022 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2022, pp.795 - 811 | - |
dc.identifier.uri | http://hdl.handle.net/10203/300903 | - |
dc.description.abstract | High-definition live streaming has experienced tremendous growth. However, the video quality of live video is often limited by the streamer's uplink bandwidth. Recently, neural-enhanced live streaming has shown great promise in enhancing the video quality by running neural super-resolution at the ingest server. Despite its benefit, it is too expensive to be deployed at scale. To overcome the limitation, we present NeuroScaler, a framework that delivers efficient and scalable neural enhancement for live streams. First, to accelerate end-To-end neural enhancement, we propose novel algorithms that significantly reduce the overhead of video super-resolution, encoding, and GPU context switching. Second, to maximize the overall quality gain, we devise a resource scheduler that considers the unique characteristics of the neural-enhancing workload. Our evaluation on a public cloud shows NeuroScaler reduces the overall cost by 22.3× and 3.0-11.1× compared to the latest per-frame and selective neural-enhancing systems, respectively. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | NeuroScaler: neural video enhancement at scale | - |
dc.type | Conference | - |
dc.identifier.wosid | 000859320200053 | - |
dc.identifier.scopusid | 2-s2.0-85138118966 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 795 | - |
dc.citation.endingpage | 811 | - |
dc.citation.publicationname | 2022 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2022 | - |
dc.identifier.conferencecountry | NE | - |
dc.identifier.conferencelocation | Amsterdam | - |
dc.identifier.doi | 10.1145/3544216.3544218 | - |
dc.contributor.localauthor | Han, Dongsu | - |
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