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Appl."],"published-print":{"date-parts":[[2019,8,31]]},"abstract":"<jats:p>Although Dynamic Adaptive Streaming over HTTP (DASH) is the pillar of multimedia content delivery mechanisms, its purely client-based adaptive video bitrate mechanisms have quality-of-experience fairness and stability problems in the existence of multiple DASH clients and highly fluctuating background traffic on the same shared bottleneck link. Varying chunk duration among different titles of multiple video providers exacerbates this problem. With the help of the global network view provided by the software-defined networking paradigm, we propose a centralized joint optimization module-assisted adaptive video bitrate mechanism that takes diversity of chunk sizes among different content into account. Our system collects possible video bitrate levels and chunk duration from DASH clients and simply calculates the optimal video bitrates per client based on the available capacity and chunk duration of each client\u2019s selected content while not invading users\u2019 privacy. By continuously following the background traffic flows, it asynchronously updates the target video bitrate levels to avoid both buffer stall events and network underutilization issues rather than bandwidth slicing, which brings about scalability problems in practice. It also guarantees fair startup delays for video sessions with various chunk duration. Our experiments clearly show that our proposed approach considering diversity of chunk duration and that background traffic fluctuations can significantly provide a better and fair quality of experience in terms of structural similarity--based video quality and startup delay compared to both purely client-based and state-of-the-art software-defined networking--based adaptive bitrate mechanisms.<\/jats:p>","DOI":"10.1145\/3337681","type":"journal-article","created":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T19:51:56Z","timestamp":1566330716000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Chunk Duration--Aware SDN-Assisted DASH"],"prefix":"10.1145","volume":"15","author":[{"given":"Ihsan Mert","family":"Ozcelik","sequence":"first","affiliation":[{"name":"Bogazici University, Bebek, Istanbul, Turkey"}]},{"given":"Cem","family":"Ersoy","sequence":"additional","affiliation":[{"name":"Bogazici University, Bebek, Istanbul, Turkey"}]}],"member":"320","published-online":{"date-parts":[[2019,8,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2736638"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964332"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBC.2018.2816789"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2733344"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2862938"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183516"},{"key":"e_1_2_1_7_1","first-page":"145","article-title":"Exponential smoothing for predicting demand","volume":"5","author":"Brown Robert G","year":"1957","unstructured":"Robert G Brown . 1957 . 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