On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

AbstractSummaryScreen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)‐driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look‐alike strategy implementing max‐min fairness.A new Quality of Experience (QoE)‐driven bandwidth allocation framework that maximizes the QoE over all users of a network provider based on their screen resolution. QoE functions built using curve fitting on datasets capturing relationship between MOS, screen characteristics, and bandwidth requirements. Proposed optimal solution able to improve overall QoE and main application‐level QoS metrics (e.g., stalls and resolution switches) compared to legacy DASH. View Figure