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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>\n            The video streaming system employs adaptive bitrate (ABR) algorithms to optimize a user\u2019s quality of experience. However, it is hard for ABR algorithms to choose the right bitrate consistently under highly dynamic bandwidth fluctuations in wild Internet. In this article, we propose a building block on the client side named\n            <jats:italic>Opportunistic Chunk Replacement Mechanism<\/jats:italic>\n            (OCRM) to help existing ABR algorithms make full use of the available bandwidth to improve the network utilization and viewing experience of users. Specifically, the servers take advantages of the spare bandwidth to opportunistically transmit high-quality chunks (called\n            <jats:italic>opportunistic chunks<\/jats:italic>\n            ) with low priority to the client, without incurring any extra delay. Then, the client player replaces the low-quality chunks with the opportunistic ones that have high quality. We compare OCRM with state-of-the-art ABR algorithms by using trace-driven experiments spanning a wide variety of quality of experience metrics and network conditions. The test results show that OCRM effectively achieves high network utilization and improves the user\u2019s viewing experience by up to 35%.\n          <\/jats:p>","DOI":"10.1145\/3488722","type":"journal-article","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T11:50:20Z","timestamp":1647085820000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Opportunistic Transmission for Video Streaming over Wild Internet"],"prefix":"10.1145","volume":"18","author":[{"given":"Jiawei","family":"Huang","sequence":"first","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Qichen","family":"Su","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Weihe","family":"Li","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Zhuoran","family":"Liu","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Changsha University, Changsha, China"}]},{"given":"Sen","family":"Liu","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Ping","family":"Zhong","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Wanchun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]},{"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}]}],"member":"320","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"2022","article-title":"Cisco visual networking index: Global mobile data traffic forecast update, 2017\u20132022","volume":"2017","author":"Forecast GMDT","year":"2019","unstructured":"GMDT Forecast. 2019. 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