Client QoE-Oriented Segment Selection for DASH

Seyedebrahimi, Mir and Peng, Xiao-Hong and Bailey, Colin (2015) Client QoE-Oriented Segment Selection for DASH. In: Proceedings of IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE, pp. 1663-1668. ISBN 978-1-5090-0154-5

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A segment selection method controlled by Quality
of Experience (QoE) factors for Dynamic Adaptive Streaming over HTTP (DASH) is presented in this paper. Current rate adaption algorithms aim to eliminate buffer underrun events by significantly reducing the code rate when experiencing pauses in replay. In reality, however, viewers may choose to accept a level of buffer underrun in order to achieve an improved level of picture fidelity or to accept the degradation in picture fidelity inorder to maintain the service continuity. The proposed rate adaption scheme in our work can maximize the user QoE in terms of both continuity and fidelity (picture quality) in DASH applications. It is shown that using this scheme a high level of quality for streaming services, especially at low packet loss rates, can be achieved. Our scheme can also maintain a best trade-off between continuity-based quality and fidelity-based quality, by determining proper threshold values for the level of quality intended by clients with different quality requirements. In addition, the integration of the rate adaptation mechanism with the scheduling process is investigated in the context of a mobile
communication network and related performances are analyzed.

Item Type: Book Section
Identification Number:
Date: 2015
Uncontrolled Keywords: rate adaptation; vide streaming; QoE; pause intensity
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cloud Computing
REF UoA Output Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Ian Mcdonald
Date Deposited: 14 Mar 2017 11:32
Last Modified: 15 Aug 2017 08:45

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