QoE space based QoE adaptation algorithm for SVC-P2P video streaming systems

In this paper, we propose a QoE Space (QS) based QoE Adaptation (QSQA) algorithm for SVC based peer-to-peer (SVC-P2P) video streaming systems. The QS records the users' experiences in terms of Mean Opinion Scores (MOS) with different content type, packet loss rate, spatial resolution, frame rate and video compression Quantization Parameter (QP). The two features of QS, the video content classification and the linear interpolation of MOS, make it can be universally used for QoE prediction in video streaming systems. The prediction accuracy of QS is also validated from these two aspects. By utilizing the initial QoE adaptation and progressive QoE adaptation, the QSQA algorithm determines layer subscription for client according to network environments, terminal conditions and video content characteristics. Extensive subjective experimental results of SVC-P2P video streaming show that QSQA obviously outperforms quality adaptation algorithms that based on Layer-by-Layer and JSVM bit stream extraction.