# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 4 Experiments
### Settings  
In the following we highlight the settings used for evaluating our technique. In our experiments, we assumed that each video, for simulation purposes, had at least one hour of playback time. Moreover, we note that abundant data of average playback history for each user is collected. We assumed that the given video genre of the corresponding histories was all identical. Furthermore, we assumed that each chunk had 2-s duration so that the entire time steps of video playback had 1800 evolution. Based on each user's playback history, the softmax function classifies each user's playback tendency using the calculated probability. The tendencies are composed as follows: infancy, middle, final, and arbitrary playback. For simplicity of simulation, the criteria of classifying playback tendency was set as 2:4:2 ratio from the initial part of a one-hour video, and descending weights were allocated for the corresponding video chunk _clicked_ (_or hop_) by the user. Note that the weights decreased from 8 to 1 in order of _click_. After the former steps were complete, average weight values per each tendency category were calculated and utilized for input of softmax function. That is, after calculating average weights value of each playback position category, the values were utilized for the input of the softmax function. The highest value among them was assumed as the user's playback tendency. Taking the softmax property into account, trivial gaps between playback weights can be drastically differentiated. Accordingly, the expected tendency of playback per user can be classified clearly and comparatively in a precise manner. To sum up, given the abundant playback history of each user, proactive loading based on user-specific playback tendency expectation enables users to experience improved QoE. Proactive loading is enabled to prevent the _freezing phenomenon_, which halts playback of video due to the request of an unprepared video chunk. Thus, the softmax-based user tendency classification system was designed for this purpose.  
Before outlining the performance evaluation, we show how the softmax classification works, as shown in Figure 6.  
To measure the experimental environment, we used the video IDs \"5Z6 XSZcV27Q\", \"5 zL3 YJKygd4\", \"S91 KmOLt-Fg\", \"HzNlrpabXw0\", and \"rvxGpkkjRyw\" on youtube. We supposed that the users watched the same quality video. In other words, we considered users' viewing patterns, which were assumed to be more significant in this paper. As a future work, different video qualities could be considered to support the DASH method. The selection of each video was based on categories, where we selected a representative video of each category: sci-fi, romance, sports, documentary, and comedy. More detailed information of the selected videos is shown in Figure 7 and Table 1.  
The initial values for learning and the table of resulting values are organized as shown in Tables 2 and 3. The priority value per category specified in Table 2 was the initial value. All initial values had the same priority. Therefore, all initial chunks had the same value of 0.1. These values were learned according to the behavioral patterns of the user, and then changed as in Table 3, which are called user-optimized values.  
Table 3 shows the priority of chunks per video category obtained by learning. The proposed technique was tested under the assumption that the network environment was 24 frames droppedper second.  
Figure 6: (Individual) probability distribution for each user preference which determines the category of users in terms of playback tendency by using the softmax process: (**a**) infancy, (**b**) middle, (**c**) final, and (**d**) arbitrary playback.