mca 18(12): e5

Research Article

Video-on-demand QoE Evaluation Across Different Age- Groups and Its Significance for Network Capacity

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  • @ARTICLE{10.4108/eai.10-1-2018.153557,
        author={Mujtaba Roshan and John A. Schormans and Rupert Ogilvie},
        title={Video-on-demand QoE Evaluation Across Different Age- Groups and Its Significance for Network Capacity},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={4},
        number={12},
        publisher={EAI},
        journal_a={MCA},
        year={2018},
        month={1},
        keywords={quality of experience, quality of service, packet loss probability, network capacity.},
        doi={10.4108/eai.10-1-2018.153557}
    }
    
  • Mujtaba Roshan
    John A. Schormans
    Rupert Ogilvie
    Year: 2018
    Video-on-demand QoE Evaluation Across Different Age- Groups and Its Significance for Network Capacity
    MCA
    EAI
    DOI: 10.4108/eai.10-1-2018.153557
Mujtaba Roshan1,*, John A. Schormans2, Rupert Ogilvie3
  • 1: Mujtaba Roshan is with the School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK. He would like to acknowledge the award of a UK Government EPSRC CASE studentship with Intergence Systems that supports his research.
  • 2: John A. Schormans is with the School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
  • 3: Intergence Systems, Old Coach House, Brewery Road, Pampisford CB22 3HG
*Contact email: m.roshan@qmul.ac.uk

Abstract

Quality of Experience (QoE) drives churn in the broadband networks industry, and good QoE plays a large part in the retention of customers. QoE is known to be affected by the Quality of Service (QoS) factors packet loss probability (PLP), delay and delay jitter caused by the network. Earlier results have shown that the relationship between these QoS factors and QoE is non-linear, and may vary from application to application. We use the network emulator Netem as the basis for experimentation, and evaluate how QoE varies as we change the emulated QoS metrics. Focusing on Video On Demand we discovered that the reported QoE may differ widely for users of different age groups, and that the most demanding age group (the youngest) can require an order of magnitude lower PLP to achieve the same QoE than is required by the most widely studied age group of users. We then used a bottleneck TCP model to evaluate the capacity cost of achieving an order of magnitude decrease in PLP, and found it be (almost always) a 3-fold increase in link capacity that was required.