## 1 Introduction  
With the advancements of smart devices and the rapid development of wired and wireless networks, our modes of entertainment and venues for information are changing rapidly. In the past, our receipt of multimedia mainly relied upon standardized broadcasts from franchise television (TV) networks. With the turn of the century, our multimedia consumption has centered around internet smartphones, as typified by the \"over-the-top\" internet-protocol (IP) services of YouTube and Netflix [1]. Because of its ubiquity and simplicity, OTT content can be viewed worldwide without TVs. According to PricewaterhouseCoopers, the global OTT market is expected to grow sharply from USD 81.6 B in 2019 to USD 156.9 B in 2024 with an annual average growth of approximately 14% [2]. Furthermore, subscription OTT services are expected to reach approximately 650 million by 2021 [3]. With this growth, the dominant OTT players such as YouTube, Netflix, Amazon Prime, and Hulu, are actively promoting entry into the global market, and Disney, Apple, Warner Media, and HBO are gaining access using their existing content. The rising OTT market is indeed becoming a fierce battleground.  
Consequently, network broadcast media and more recent TV-over-IP enterprises have experienced heavy competition [4; 5]. On the other hand, internet-service providers (ISP) increasingly find themselves in conflicts with OTT media providers because of bandwidth- and fee-related issues. Because OTT services consume a vast amount of network resources, ISPs seek to bolster their profits to support growth [6]. Furthermore, consumers increasingly demand higher quality of service (QoS) from their ISPs [7] who desperately need to expand their throughput capabilities [8]. Often, the ISPs respond to bandwidth overloads by limiting the amount of throughput (i.e., throttling) based on the OTT service in demand. This process is most often reactionary, inevitably creating surges of consumer complaints. To balance this vicious cycle of competing demands, the ISPs require better and more-timely consumer OTT-usage analysis capabilities, so that they can better mitigate network performance issues while balancing customer demand. Furthermore, utilizing a sound and trustworthy tool such for this purpose would put the ISPs in a better position to negotiate with OTT providers [9].  
Traffic analysis models have been widely researched and utilized for this purpose in conventional hypertext-transfer-protocol (HTTP) mobile-network environments. Machine learning has been key to their success. However, only a few academic studies have focused on OTT content in the context of strategic service provision [9]. In this study, we analyze network consumption patterns based on consumers' OTT-usage patterns, confirming that a combination of machine- and deep-learning capabilities can achieve the highest accuracy. Additionally, by mitigating the time and resource requirements of deep learning, we provide a novel MetaCost-based framework related to OTT user analysis that can reduce the time required for analysis while exploiting the technology's high accuracy. This framework drastically reduces the analysis workload, making the process very efficient and timely so that ISPs can achieve instantaneous status and influence over OTT service demands.  
This paper is structured as follows. In Section 2, we examine why the analysis of OTT-related trends and data-usage patterns is critical. We also justify the application of machine and deep learning to this pursuit with a review of previous traffic-analysis studies. In Section 3, we fully describe the OTT user-analysis framework. Section 4 presents a discussion of our experimental results. Finally, Section 5 presents the conclusions of this study with further research directions.