{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:10:09Z","timestamp":1750209009193,"version":"3.41.0"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,9,7]],"date-time":"2019-09-07T00:00:00Z","timestamp":1567814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Data and Information Quality"],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>\n            With internet video gaining increasing popularity and soaring to dominate network traffic, extensive studies are being carried out on how to achieve higher Quality of Experience (QoE) with the delivery of video content. Associated with the chunk-based streaming protocol, Adaptive Bitrate (ABR) algorithms have recently emerged to cope with the diverse and fluctuating network conditions by dynamically adjusting bitrates for future chunks. This inevitably involves predicting the future throughput of a video session. Some of the session features like Internet Service Provider (ISP), geographical location, and so on, could affect network conditions and contain helpful information for this throughput prediction. In this article, we consider how our knowledge about the session features can be utilized to improve ABR quality via customized parameter settings. We present our ABR-independent, QoE-driven, feature-based partition method to classify the logged video sessions so that different parameter settings could be adopted in different situations to reach better quality. A\n            <jats:italic>variation<\/jats:italic>\n            of Decision Tree is developed for the classification and has been applied to a sample ABR for evaluation. The experiment shows that our approach can improve the average bitrate of the sample ABR by 36.1% without causing the increase of the rebuffering ratio where 99% of the sessions can get improvement. It can also improve the rebuffering ratio by 87.7% without causing the decrease of the average bitrate, where, among those sessions involved in rebuffering, 82% receives improvement and 18% remains the same.\n          <\/jats:p>","DOI":"10.1145\/3309682","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T12:10:26Z","timestamp":1568031026000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Adaptive Video Streaming through Session Classification"],"prefix":"10.1145","volume":"11","author":[{"given":"Zahaib","family":"Akhtar","sequence":"first","affiliation":[{"name":"University of Southern California, CA, USA"}]},{"given":"Anh Minh","family":"Le","sequence":"additional","affiliation":[{"name":"University of Windsor, Windsor, ON, Canada"}]},{"given":"Yun Seong","family":"Nam","sequence":"additional","affiliation":[{"name":"Purdue University, IN, USA"}]},{"given":"Jessica","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Windsor, Windsor, ON, Canada"}]},{"given":"Ramesh","family":"Govindan","sequence":"additional","affiliation":[{"name":"University of Southern California, CA, USA"}]},{"given":"Ethan","family":"Katz-Bassett","sequence":"additional","affiliation":[{"name":"Columbia University, New York, NY, USA"}]},{"given":"Sanjay","family":"Rao","sequence":"additional","affiliation":[{"name":"Purdue University, IN, USA"}]},{"given":"Jibin","family":"Zhan","sequence":"additional","affiliation":[{"name":"Conviva, Foster City, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,9,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3230543.3230558"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486025"},{"key":"e_1_2_1_3_1","unstructured":"L. Breiman J. H. Friedman R. A. Olshen and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth Belmont.  L. Breiman J. H. Friedman R. A. Olshen and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth Belmont."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2910017.2910603"},{"volume-title":"Proceedings of Packet Video Workshop.","author":"Cicco L. De","key":"e_1_2_1_5_1","unstructured":"L. De Cicco , V. Caldaralo , V. Palmisano , and S. Mascolo . 2013. ELASTIC: A client-side controller for dynamic adaptive streaming over HTTP (DASH) . In Proceedings of Packet Video Workshop. L. De Cicco, V. Caldaralo, V. Palmisano, and S. Mascolo. 2013. ELASTIC: A client-side controller for dynamic adaptive streaming over HTTP (DASH). In Proceedings of Packet Video Workshop."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2018436.2018478"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2155555.2155557"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626296"},{"volume-title":"Proceedings of the USENIX NSDI. 137--150","author":"Jiang J.","key":"e_1_2_1_9_1","unstructured":"J. Jiang , V. Sekar , H. Milner , D. Shepherd , I. Stoica , and H. Zhang . 2016. CFA: A practical prediction system for video QoE optimization . In Proceedings of the USENIX NSDI. 137--150 . J. Jiang, V. Sekar, H. Milner, D. Shepherd, I. Stoica, and H. Zhang. 2016. CFA: A practical prediction system for video QoE optimization. In Proceedings of the USENIX NSDI. 137--150."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2413176.2413189"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2014.140405"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/insr.12016"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098843"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2016.7838090"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022643204877"},{"volume-title":"Programs for Machine Learning","author":"Quinlan J. R.","key":"e_1_2_1_16_1","unstructured":"J. R. Quinlan . 1993. C4.5 : Programs for Machine Learning . Morgan Kaufmann Publishers . J. R. Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers."},{"key":"e_1_2_1_17_1","unstructured":"Spark Machine Learning Library. 2017. Spark Machine Learning Library (MLlib) Guide. Retrieved from spark.apache.org\/docs\/latest\/mllib-guide.html.  Spark Machine Learning Library. 2017. Spark Machine Learning Library (MLlib) Guide. Retrieved from spark.apache.org\/docs\/latest\/mllib-guide.html."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2229087.2229093"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934898"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2413176.2413190"},{"volume-title":"Proceedings of the IEEE IM. 131--138","author":"van der Hooft J.","key":"e_1_2_1_21_1","unstructured":"J. van der Hooft , S. Petrangeli , M. Claeys , J. Famaey , and F. De Turck . 2015. A learning-based algorithm for improved bandwidth awareness of adaptive streaming clients . In Proceedings of the IEEE IM. 131--138 . J. van der Hooft, S. Petrangeli, M. Claeys, J. Famaey, and F. De Turck. 2015. A learning-based algorithm for improved bandwidth awareness of adaptive streaming clients. In Proceedings of the IEEE IM. 131--138."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787486"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2910018.2910655"}],"container-title":["Journal of Data and Information Quality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3309682","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3309682","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:57:58Z","timestamp":1750208278000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3309682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,7]]},"references-count":23,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,12,31]]}},"alternative-id":["10.1145\/3309682"],"URL":"https:\/\/doi.org\/10.1145\/3309682","relation":{},"ISSN":["1936-1955","1936-1963"],"issn-type":[{"type":"print","value":"1936-1955"},{"type":"electronic","value":"1936-1963"}],"subject":[],"published":{"date-parts":[[2019,9,7]]},"assertion":[{"value":"2018-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-01-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-09-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}