4.5 Article

Enhancing QoE for video streaming in MANETs via multi-constraint routing

Journal

WIRELESS NETWORKS
Volume 24, Issue 1, Pages 235-256

Publisher

SPRINGER
DOI: 10.1007/s11276-016-1325-9

Keywords

Link-stability; Link loss factor; Video streaming; QoE-centric routing; Quality of service; Mean opinion score; Emulation

Funding

  1. Marie Curie Fellowship - European Commission [PCIG11-GA-2012-321980]
  2. EU TagItSmart! Project [H2020-ICT30-2015-688061]
  3. EU-India REACH Project [ICI+/2014/342-896]
  4. Italian MIUR-PRIN TENACE Project [20103P34XC]
  5. project Tackling Mobile Malware with Innovative Machine Learning Techniques - University of Padua
  6. project Physical-Layer Security for Wireless Communication - University of Padua
  7. project Content Centric Networking: Security and Privacy Issues - University of Padua

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In this paper, we propose a Multi-Constraint Quality-of-Experience (QoE) centric Routing (MCQR) technique for efficient real-time video streaming over Mobile Ad-hoc Networks (MANETs). The dynamic estimation of link quality metric during MCQR route discovery process is the key of enhancing QoE at end user. MCQR limits end-to-end delay and jitter of received data packets by selecting long lifetime routes with maximum permissible delay. This minimizes the distortion caused fluctuations in perceived video. To prove the effectiveness of MCQR for real-time video streaming, we have designed a hybrid MANET consisting of simulated nodes and physical machines. In our hybrid network, the source and destination nodes are video traffic generators and receivers running on physical devices. Emulation results are obtained using real-time video streaming over high fidelity virtual wireless network, and conforming that the MCQR protocol provides satisfactory QoE to end users as compared to existing similar techniques. The packet delivery ratio of MCQR is higher than 90 % and its mean opinion score is higher than three (i.e., satisfactory) even when mobility and shadowing effects are kept high in target network scenarios. These improvements are achieved along with still maintaining similar or better routing overhead and processing delays.

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