# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 1 Introduction  
With the growing development of communication and multimedia technology, more and more people use the Internet to obtain information online, such as browsing the web, listening to music, watching videos, downloading files, and so on. In this case, understanding and evaluating the network performance becomes an important task. Traditionally, network operation and maintenance centers use network technology-centric metrics in terms of quality of service (QoS), such as transmission delay, packet loss rate, etc., to measure the network quality. However, it has been demonstrated that the QoS primarily targets at improving the service quality with respect to application-level technical parameters, which lacks sufficient consideration of the user's actual perceptions and feelings [1]. To solve this problem, the user-centric measurement of service quality with the notion of quality of experience (QoE) is introduced, which has drawn much attention in both academia and industry [2; 3; 4]. Generally, the assessment of QoE can be done in both subjective and objective ways [5], where subjective evaluation is usually implemented by questionnaires and rating scales [6], can be treated as the more direct and reliable way to evaluate QoE scores. However, it is time-consuming, costly, and inconvenient [7]. Another assessment appraoch of QoE, objective assessment, uses predefined models to approximate the subjective QoE estimation without human involvement [5]. The existing objective QoE assessment models can be roughly divided into two categories, namely, signal fidelity based and QoS-based. Generally, playback operations are needed when applying the signal fidelity methods, but it is difficult in most streaming services. Thus, the signal fidelity methods cannot be widely applied even if they are highly accurate. In terms of the QoS-based model, the aim isto find a mapping relation between QoS and QoE. As QoS is described by the network parameters, such as network delay, packet loss rate, and network flow throughput rate; once the mapping relation is built, the QoS-based QoE quantization will be established. Owing to this idea, substantial efforts have been made by engineers and scholars when exploring the QoS-based model, where some works have been done for specific types of network service, such as video stream service [8; 9; 10; 11; 12], Internet of Things [13], Instant Messaging [14; 15; 16], File Sharing [17], and so on, where proprietary features or parameters are also used in. In [8], the authors proposed a QoE formulation for encrypted YouTube video stream, video source quality-oriented factors like video codec, codec profile, video resolution, bitrate, and video viewing-oriented factors like initial buffering latency, stalling ratio score are both considered into establishing the QoE equation. [PERSON] et al. also proposed a QoE evaluation model by using relevant QoS parameters for video service [10], where the QoS formula takes into account the parameters of packet loss, burst level, packet jitter, packet delay, and bandwidth, as well as the weights for each parameters. In [11], a joint framework of QoS and QoE for video transmission over wireless multimedia sensor networks (WMSNs) is proposed, which can support QoS in WMSNs with a lightweight error concealment and utilize the maximum network throughput. The authors of [12] analyzed the streaming video service performance with both the subjective and objective QoS factors (network delay, jitter, and packet loss rate) that affect QoE, which is an inspiration in the processes of both forming QoE model and doing experiments. [PERSON] et al. developed a regression equation between QoE and the principal QoS parameters of IoT applications [13].