## 1 Introduction  
Watching online videos has become one of the most popular user activities due to the fast development of multimedia services, such as online learning, mobile streaming, Internet TV, etc. According to [1], the average online video viewing hours have increased 68% globally in 2019. As of 2021, streaming video accounts for over 53% of all traffic on the Internet [2]. HTTP Adaptive Streaming (HAS) has become the standard solution for multimedia streaming over the Internet nowadays [3]. In HAS, a video is firstly encoded into multiple versions corresponding to different quality levels. Then, each version is divided into short chunks called _segments_. Every segment has the same playback duration commonly ranging from 2 s to 10 s. Segments with suitable versions are delivered to the user device according to the estimated throughput. Because of throughput fluctuations, the segments' versions are often changing, resulting in quality variations during a streaming session. In addition, stalling events may occur when segments do not reach the user device before their playback deadlines. To reduce dramatic quality variations and sudden stalling events, a special stalling event, which is called an initial delay, is inserted before starting the video playback [4]. However, all these factors are well-known to cause negative impacts on the Quality of Experience (QoE) perceived by users [5]. Therefore, to provide the highest possible quality to users, it is imperative to assess the QoE of streaming sessions taking into account the joint impacts of the factors. In the literature, many models have been proposed for predicting the QoE of HAS streaming sessions [6; 7; 8; 9; 10; 11; 12; 13]. These models are different in the number of considered factors, modeling approach, etc. Moreover, they are usually evaluated over a small number of databases (mostly one or two), making it difficult to realize the true effectiveness of existing QoE models [6; 7; 8; 9; 10; 11]. Recently, many QoE databases have been made available to the public with different settings of the impact factors, viewing devices, codecs such as H.264 [14] and HEVC [15], session durations, and session generation methods [16; 17; 18; 19; 20; 21; 22]. To the best of our knowledge, there have been no extensive evaluations of QoE models in the literature. Thus, it is challenging for service providers and practitioners to choose appropriate QoE model in practice. In addition, it is worth emphasizing that the evaluation of any QoE model should be carried out over different databases to truly reveal the model's performance. In this paper, we present a comprehensive evaluation of thirteen existing QoE models (see Table 1) for HTTP Adaptive Streaming over up to twelve open databases. Through experiment results, various findings and suggestions are provided with regard to the performance of the models. In particular, we are able to obtain the following important observations. * All considered models yield better performance on H.264-encoded streaming sessions than HEVC-encoded ones. Surprisingly, even the models taking into account HEVC characteristics such as _P.1203_ and _KSQI_ are not very effective for HEVC-encoded sessions. * To obtain the high and stable performance for different devices, it is recommended to use Mean Opinion Score (MOS) and Video Multi-method Assessment Fusion (VMAF) to calculate segment quality values. * Besides quality variations and stalling events, temporal relations between impairment events should be also considered in QoE models. * The use of multiple statistics as model inputs is indispensable to fully represent quality variations and stalling events in a streaming session. However, it is also found that complex models that contain more statistics do not always lead to better performance. * Among the considered models, the _LSTM_ model [10] is the best one since it provides the highest and most stable performance across viewing devices and session durations.