# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 1 Introduction  
Media content distribution today is mainly based on online live streaming services, such as Youtube, Netflix, and Hulu. The major content distribution services are based on an over-the-top (OTT) service technology, which allows for the provision of a variety of Internet-based media content by third-party operators, such as the major communication and broadcasting operators. As the demand on OTT streaming services increases constantly, provisioning the Quality of Experience (QoE) for individual users becomes more important than ever [1]. OTT services that guarantee high QoE by considering individual user requirements are primarily sensitive to service delays, which severely degrade user satisfaction [2]. Therefore, various QoE provisioning techniques have been proposed in the literature to provide continuous multimedia services. In the prior related research work, QoE-related studies have considered variables in the metrics that would be only associated with the communications environment. For OTT and online streaming services, however, one additional variable is added: the user. Because users can take multiple actions during the progression of a video stream, at the level of seconds, various complex variables are added that may affect the QoE [3; 4]. For example, this characteristic has been represented in a limiting phenomenon as a result of interest degradation caused by prolonged watching, and as captured in the single stimulus continuous quality evaluation (SSCQE) methods [5]. As such, and as this limiting point is reached, a user typically passes the video over the current part of progress to another interesting part. The adaptive streaming method, one of the existing approaches to ensuring QoE, fosters its limitations under the preceding scenario. Because the quality only changes depending on the environment in which video is played, and because the order in which a video is received is sequential, it is often the case that the parts of interest to users are not yet downloaded. As a result, users are made to feel uncomfortable by the resulting delay in obtaining the next chunk corresponding to the video part of interest. Under suchcircumstances, the previous studies trying to address QoE through pure quality of service environment metrics optimization are not suitable to address the users' behavioral patterns and reactions, mainly because they only focus on the users' environment, rather than users' direct action (e.g., skipping) [6; 7]. In particular, the existing techniques to address the problem do not guarantee a high QoE. To this end, this work is mainly focused on a new method that provides a personalized online live video streaming using multinominal classification. Basically, the suggested method considers users' viewing patterns to reduce the loading delay. To provide a customized loading order for each user, the method collects users' viewing pattern (e.g., skipping) data generated while watching the video. The method predicts users' action probability for each chunk obtained by the collected data and the softmax algorithm. The end goal of this classification is to optimize the user experience. First, we start by describing several previous studies for streaming transport techniques to improve the quality of streaming services considering the state of the network. Then, while addressing the shortcomings set forth earlier, we also propose an optimized streaming transport technique for users. The technique is called _user-based adaptive streaming_ (UAS). The proposed technique classifies users' video viewing patterns based on a softmax algorithm, and then receives the chunk of video by priorities based on the analysis results. This investigation reflects not only the changing state of the network, but also the viewing patterns of users. These findings enable the partial units (chunks) to be transmitted efficiently. This will allow us to satisfy the different QoE criteria of each user. The rest of this paper is organized as follows. Section 2 describes the existing relevant studies.