{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T06:23:47Z","timestamp":1770531827429,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Video streaming service delivery is a challenging task for mobile network operators. Knowing which services clients are using could help ensure a specific quality of service and manage the users\u2019 experience. Additionally, mobile network operators could apply throttle, traffic prioritization, or differentiated pricing. However, due to the growth of encrypted Internet traffic, it has become difficult for network operators to recognize the type of service used by their clients. In this article, we propose and evaluate a method for recognizing video streams solely based on the shape of the bitstream on a cellular network communication channel. To classify bitstreams, we used a convolutional neural network that was trained on a dataset of download and upload bitstreams collected by the authors. We demonstrate that our proposed method achieves an accuracy of over 90% in recognizing video streams from real-world mobile network traffic data.<\/jats:p>","DOI":"10.3390\/s23052548","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:10:46Z","timestamp":1677463846000},"page":"2548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Video Stream Recognition Using Bitstream Shape for Mobile Network QoE"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3096-6549","authenticated-orcid":false,"given":"Darius","family":"Chmieliauskas","sequence":"first","affiliation":[{"name":"Department of Computer Science and Communications Technologies, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5039-0607","authenticated-orcid":false,"given":"\u0160ar\u016bnas","family":"Paulikas","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Communications Technologies, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","unstructured":"Ericsson (2021). 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