Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles in a segment to be requested before playback. All existing works either focus on tile prediction or on tile computing and delivering, overlooking the facts that these three tasks have to share the same duration and the quality of experience (QoE) depends on the worst performance of them. In this paper, we jointly optimize the duration of the observation window for prediction and the durations used for computing and communication to maximize the QoE of watching a VR video. We find the global optimal solution by decomposing the original problem equivalently into subproblems, with which we find prediction-limited or resource-limited region. Simulation results demonstrate the gain of the optimized durations by using two existing prediction methods with a real dataset.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Matching Prediction to Communication and Computing for Proactive VR Video Streaming


    Contributors:
    Wei, Xing (author) / Yang, Chenyang (author)


    Publication date :

    2020-05-01


    Size :

    257562 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Duration-Squeezing-Aware Communication and Computing for Proactive VR

    Wei, Xing / Yang, Chenyang / Han, Shengqian | IEEE | 2021


    Rate allocation for prediction drift reduction in video streaming

    Prades-Nebot, J. / Cook, G.W. / Delp, E.J. | IEEE | 2005


    Rate Allocation for Prediction Drift Reduction in Video Streaming

    Prades-Nebot, J. / Cook, G. W. / Delp, E. J. | British Library Conference Proceedings | 2005


    Video streaming anomaly detection

    LEI OLIVER / SLIUSAR VLADISLAV | European Patent Office | 2023

    Free access

    QoE-driven Link Quality Prediction for Video Streaming in Mobile Networks

    Wang, Yitu / Kudo, Riichi / Aoki, Yuya et al. | IEEE | 2022