Inferring video QoE in real time

• Published in: IEEE network Vol. 25; no. 1; pp. 4 - 13
• Main Authors: Venkataraman, Mukundan, Chatterjee, Mainak
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Conduction; Delay; Human; Human subjects; Internet; Jitter; Partitions; Perception; Quality of service; Real time; Real time systems; Streaming media; Streams; Surveys; Video communication; Weight reduction
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.2011.5687947

Inferring the subjective perception of a video stream in real time continues to be a stiff problem. This article presents MintMOS: a lightweight, no-reference, loadable kernel module to infer the QoE of a video stream in transit and offer suggestions to improve it. MintMOS revolves around one-time offline construction of a k-dimensional space, which we call the QoE space. A QoE space is a known characterization of subjective perception for any k parameters (network dependent/ independent) that affect it. We create N partitions of the QoE space by generating N video samples for various values of the k parameters and conducting subjective surveys using them. Every partition then has an expected QoE associated with it. Instantaneous parameters of a real-time video stream are compared to the precomputed QoE space to both infer and offer suggestions to improve QoE. Inferring QoE is a lightweight algorithm that runs in linear time. We implemented MintMOS by creating an actual QoE space using three parameters and 27 partitions by conducting surveys with 77 human subjects. In a second set of surveys using 13 video clips, MintMOS's predictions were compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions.