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Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity\u2010guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP\u2010hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state\u2010of\u2010the\u2010art schemes.<\/jats:p>","DOI":"10.1155\/2022\/5562995","type":"journal-article","created":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T14:35:10Z","timestamp":1641393310000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Popularity\u2010Guided Cost Optimization for Live Streaming in Mobile Edge Computing"],"prefix":"10.1155","volume":"2022","author":[{"given":"Tao","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8772-0670","authenticated-orcid":false,"given":"Kunxin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruomei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0400-9366","authenticated-orcid":false,"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"volume-title":"Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast Update","year":"2013","author":"Pepper R.","key":"e_1_2_12_2_2"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.07.019"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2705720"},{"key":"e_1_2_12_5_2","doi-asserted-by":"crossref","unstructured":"BilalK.andErbadA. 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