Please use this identifier to cite or link to this item:
|Title:||Integrated optimization of video server resource and streaming quality over best-effort network||Authors:||Yu, H.
|Keywords:||Forward error correction (FEC) protection
|Issue Date:||2009||Citation:||Yu, H., Chang, E.-C., Ooi, W.T., Chan, M.C., Cheng, W. (2009). Integrated optimization of video server resource and streaming quality over best-effort network. IEEE Transactions on Circuits and Systems for Video Technology 19 (3) : 374-385. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSVT.2009.2013501||Abstract:||A video streaming server needs to adapt its source/channel encoding parameters (or configurations) to changes in network conditions and to differences in users' connection profiles. The adaptation can be achieved by adjusting parameters such as frame rate, error protection ratio, and resolution. Ideally, the server should adapt the serving configurations with respect to the current network and user conditions to improve received video quality. However, adaptations that optimize playable frame rate require intensive computation, and storing all possible configurations requires a tremendous amount of storage. This brings forth the issues of how to obtain good video quality and reduce server resources usage at the same time. We address this issue in this paper. Our approach is based on the observation that transcoding between certain configurations can be performed very efficiently. We propose a framework to compute a set of configurations to store on the server by considering two opposing goals: (a) maximizing expected received quality of the video, and (b) minimizing server resource usage by lowering transcoding cost and expected number of switches between configurations. The second objective also reduces the number of configurations, and therefore reduces the total storage required. Our framework models the relationship among different configurations in a partial order, formulates the search of a good set of configurations as an energy minimization problem, and we use techniques in image segmentation to solve the problem. Experimental results show that our framework relieves the server load and increases the number of clients served, while only slightly reducing the expected frame rate. © 2006 IEEE.||Source Title:||IEEE Transactions on Circuits and Systems for Video Technology||URI:||http://scholarbank.nus.edu.sg/handle/10635/43131||ISSN:||10518215||DOI:||10.1109/TCSVT.2009.2013501|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 9, 2021
WEB OF SCIENCETM
checked on Apr 9, 2021
checked on Mar 30, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.