# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 2 Related Works
### In-Network QoE Models  
In [21], the authors proposed an In-Network QoE Measurement Framework (IQMF). The user feedback was not considered as an input parameter in this scheme; however, the streams are monitored within the network. Two QoE metrics are adopted by IQMF for measuring experience: (1) quality of video, and (2) switching impact over HTTP Adaptive Streams. IQMF offers those measurements for QoE as a service through an API (Figure 1). This service can be provided to a content distributor or a network provider. Leveraging SDN allows the control plane to interact with IQMF framework, thus providing more flexibility to analyze and measure a participant's QoE. It also enables IQMF to utilize traffic managements dynamically and to provide scalability for deploying more measurement agents. IQMF interacts with the OpenFlow controller that maintains the forwarding behavior of the network to ensure that all necessary information about flow duplications is provided for better monitoring of QoE. The QoE measurement framework operates by filtering HTTP packets in the traversing traffic. It then identifies HTTP GET requests, and examines those requests for identification of manifest files such as Media Presentation Description (MPD) files. The MPD parser extracts information from the MPD file--different representations that include references to different resolutions, quality multitude, and playback codecs. The measurement engine then merges the parsed information with supplementary details from the HTTP packet filter in order to monitor the behavior of the user whilst playback continues. Another model, described in [22], aims to enhance the capabilities of Dynamic Adaptive Streaming over HTTP (DASH)--a standard for multimedia streaming that changes the quality of content presentation automatically in accordance with network conditions. In this research, the authors took into consideration the QoE as perceived by the user, and then integrated user perception with dynamic changes in the content. Such enhancements will provide more efficient QoE measurements and increase positive feedback by the users. This model allows automated estimation of MOS measurements from QoS parameters such as video bitrate, video frame rate, and video quantization parameter. The following three metrics were used in this model:  
1. Buffer underflow/overflow: to prevent freezing images and losing packets, buffer thresholds were specified. TCP is also used for reliable transmission. 2. Frequencies and amplitude of switching quality: the frequency of quality switches of the represented content was identified as one of factors affecting QoE. 3. QoS media parameters: the parameters associated with the content of media. It was found through experiments that it took several seconds to measure presentation intervals that are affected by media parameters. However, it takes more time intervals in terms of switching quality and re-buffering, thus impacting QoE. \"The representation quality switch rate required a recursive approach where the MOS is calculated on the basis of previous MOS variations in order to take into account the entity of the quality switch in addition to the rate\" [22]. This model has shown potential in enhancing the DASH logic of adaptation capabilities for selecting the best video quality levels, through integrating the QoE monitoring. The OpenE2 EQoS model, discussed in [20], aimed to introduce QoS into IP multicasting using SDN for flexible control management of network environment. In this approach, the OpenFlow protocol was adopted to allow the controller to monitor IP multicasting statistics for each flow and to provide end-to-end QoS. The system makes use of the Additive Increase/Multiplicative Decrease (AIMD) algorithm to enhance adaptive learning of efficient bandwidth utilization over time. An \(N\)-dimensional statistical algorithm is used in this approach to redirect low-priority traffic packets from overly crowded links while maintaining priority for multimedia packets.