Adaptive resource allocation for QoE-aware mobile communication networks

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Seyedebrahimi, Mirghiasaldin ; Peng, Xiao-Hong ; Harrison, Rob (2014)
  • Publisher: IEEE

A real-time adaptive resource allocation algorithm considering the end user's Quality of Experience (QoE) in the context of video streaming service is presented in this work. An objective no-reference quality metric, namely Pause Intensity (PI), is used to control the priority of resource allocation to users during the scheduling process. An online adjustment has been introduced to adaptively set the scheduler's parameter and maintain a desired trade-off between fairness and efficiency. The correlation between the data rates (i.e. video code rates) demanded by users and the data rates allocated by the scheduler is taken into account as well. The final allocated rates are determined based on the channel status, the distribution of PI values among users, and the scheduling policy adopted. Furthermore, since the user's capability varies as the environment conditions change, the rate adaptation mechanism for video streaming is considered and its interaction with the scheduling process under the same PI metric is studied. The feasibility of implementing this algorithm is examined and the result is compared with the most commonly existing scheduling methods.
  • References (18)
    18 references, page 1 of 2

    [1] Cisco Systems, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013-2018., San Jose, CA, USA: Cisco Systems, Inc., Feb. 2014.

    [2] 3GPP TS 26.247, “Transparent end-to-end Packet-switched Streaming Service (PSS); Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH)”, Rel. 12, March 2014.

    [3] M. Seyedebrahimi, C. Bailey, and X.-H. Peng, "Model and Performance of a No-Reference Quality Assessment Metric for Video Streaming," IEEE Transactions on Circuits and Systems for Video Technology, vol.23, no.12, (Dec. 2013), pp.2034-2043.

    [4] M. Seyedebrahimi, X.-H. Peng, and R. Harrison, "A Quality Driven Framework for Adaptive Video Streaming in Mobile Wireless Networks,” Accepted for IEEE WCNC 2014.

    [5] 3GPP LTE and LTE-Advanced Technology,

    [6] 3GPP TS 26.233, “Transparent end-to-end Packet-switched Streaming Service (PSS); General Description”, Rel. 11, April 2014.

    [7] L. De Cicco and S. Mascolo, “An adaptive video streaming control system: modeling, validation, and performance evaluation”, IEEE/ACM Trans. Netw. 22, 2 (April 2014), 526-539.

    [8] J. Wang, T.Z.J. Fu, D.M. Chiu; Z.B. Lei, "Perceptual quality assessment on B-D tradeoff of P2P assisted layered video streaming," Visual Communications and Image Processing (VCIP), 2011 IEEE , vol., no., pp.1,4, 6-9 Nov. 2011.

    [9] Subjective Audiovisual Quality Assessment Methods for Multimedia Applications, ITU-T Rec. P.911, 1998.

    [10] K. Seshadrinathan, R. Soundararajan, A.C Bovik, and L.K Cormack, "Study of Subjective and Objective Quality Assessment of Video," Image Processing, IEEE Transactions on , vol.19, no.6, pp.1427,1441, June 2010.

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