URI | http://purl.tuc.gr/dl/dias/60A01B35-E3F5-47F8-9CBF-6F1519266872 | - |
Identifier | https://doi.org/10.1109/TMM.2017.2713642 | - |
Identifier | https://ieeexplore.ieee.org/document/7944640 | - |
Language | en | - |
Extent | 12 pages | en |
Title | H.264 and H.265 video bandwidth prediction | en |
Creator | Kalampogia Athina | en |
Creator | Καλαμπογια Αθηνα | el |
Creator | Koutsakis Polychronis | en |
Creator | Κουτσακης Πολυχρονης | el |
Publisher | Institute of Electrical and Electronics Engineers | en |
Content Summary | The explosive growth of multimedia applications renders the efficiency of network resource allocation a problem of major importance. The burstiness of video traffic, in particular, calls for traffic control solutions that will help prevent significant packet losses. Such losses can lead to unacceptable quality of service (QoS) and quality of experience (QoE) to users. In this paper, we focus on a large variety of H.264-and H.265-encoded video traces with different GoP patterns. Different versions of each trace, in low, medium, and high quality have been used in our study.We evaluate the accuracy of an existing video traffic prediction approach for the size of B-frames, and we propose a new Markovian model that predicts B-frames' sizes with significantly higher accuracy. B-frame size prediction can be used in order to reduce bandwidth requirements and smooth the encoded video stream, by selective B-frame dropping, when the model predicts larger upcoming Bframe traffic than the network can handle. | en |
Type of Item | Peer-Reviewed Journal Publication | en |
Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
License | http://creativecommons.org/licenses/by/4.0/ | en |
Date of Item | 2019-10-18 | - |
Date of Publication | 2018 | - |
Subject | Bandwidth prediction | en |
Subject | H.264, H.265 | en |
Subject | Traffic modeling | en |
Subject | Video streaming | en |
Bibliographic Citation | A. Kalampogia and P. Koutsakis, "H.264 and H.265 video bandwidth prediction," IEEE Trans. Multimed., vol. 20, no. 1, pp. 171-182, Jan. 2018. doi: 10.1109/TMM.2017.2713642 | en |