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- W3118134516 abstract "With the explosive growth of video traffic, the traditional Cloud-based video streaming network has been difficult to meet the video services' requirements of low latency and high bandwidth. Therefore, Edge Computing with low latency and Dynamic Adaptive Streaming over HTTP (DASH) with dynamic adaptive changing video bitrate versions are utilized to optimize video streaming service. However, the edge server deployed next to the base station is not an exclusive tool for video streaming optimization, and its processing ability is limited. Therefore, in some request intensive scenarios such as shopping malls, schools and residential areas, when the server receives a large amount of video segment requests, it cannot process all video segment requests at the same time. The existing DASH-video service optimization methods with Edge Computing did not consider how to schedule requests which exceed the available processing ability of the edge server. Moreover, the collaboration of edge servers has not been made full use in the existing methods. In view of these challenges, we propose a multi-request scheduling and collaborative service processing method (MRSCP) for DASH-video in Cloud-Edge network. The cache placement and update, multi-request scheduling, and collaborative caching and transcoding are considered in our method. Extensive experiments are conducted to validate the efficiency and effectiveness of our method." @default.
- W3118134516 created "2021-01-05" @default.
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- W3118134516 date "2020-10-01" @default.
- W3118134516 modified "2023-09-25" @default.
- W3118134516 title "Multi-Request Scheduling and Collaborative Service Processing for DASH-Video Optimization in Cloud-Edge Network" @default.
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- W3118134516 doi "https://doi.org/10.1109/cloud49709.2020.00087" @default.
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