In the context of end-user quality improvement, previous work has proposed inclusion of different application-level network functions for negotiation and Quality-of-Service (QoS) optimization decision making . However, SDN has emerged as a networking paradigm in which the data and control planes are decoupled, and the control is logically centralized and programmable via standardized interfaces. The OpenFlow protocol provides an interface between the SDN controllers and the infrastructure elements. The interfaces between the control and the application layer provide the means for the applications to use network services and capabilities as needed, without knowing the network specifics, such as network topology. Thus, applications can issue requests (at the application layer), which are translated by the control layer to device-specific configurations. As the control layer in SDNs has a global view on the network topology graph, it is possible to implement control applications that use traditional graph optimization algorithms for traffic engineering. Such examples include the SDN-based architecture to optimize flows and user associations in wireless mesh networks , and the OpenFlow architecture for balancing the load between servers while considering the network capacity. While these works optimize flows with the goal to maximize the network throughput or minimize communication delays, it is not yet clearly quantified how the network parameters affect the perceived quality in video streaming services. From these studies it becomes evident that an understanding of the correlation between QoS, i.e. the network-related factors, and QoE is necessary in order to maximize quality.
The main focus of research in this area so far is on the rate adaptation algorithms and an evaluation in mobile heterogeneous environments . However, the evaluation in these works primarily focuses on objective quality indicators (e.g. stalling/re-buffering events, number of quality switches) without assessing their synergistic impact on the actual QoE . Indeed, MPEG-DASH and its relationship to QoE are not yet well researched; first ideas on this topic are based on objective metrics not exactly reflecting QoE. According to its definition, QoE is influenced by user expectations, context, and personal preferences. Therefore, capturing and understanding end-users’ QoE goes beyond purely measuring video quality . To the best our knowledge, there are no studies to study the cross-layer synergistic cooperation and optimisation between the network architecture, the network virtualisation controls and functions, and the optimal streaming client adaptation to optimize the overall QoE of the end-users.
