You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks 
Chapter Title:  C. END-USER BEHAVIOR AND OSN RELATIONSHIPS 
1. Abstract of Cited Paper (BIB001):  One of the most popular user activities on the Web is watching videos. Services like YouTube, Vimeo, and Hulu host and stream millions of videos, providing content that is on par with TV. While some of this content is popular all over the globe, some videos might be only watched in a confined, local region. In this work we study the relationship between popularity and locality of online YouTube videos. We investigate whether YouTube videos exhibit geographic locality of interest, with views arising from a confined spatial area rather than from a global one. Our analysis is done on a corpus of more than 20 millions YouTube videos, uploaded over one year from different regions. We find that about 50% of the videos have more than 70% of their views in a single region. By relating locality to viralness we show that social sharing generally widens the geographic reach of a video. If, however, a video cannot carry its social impulse over to other means of discovery, it gets stuck in a more confined geographic region. Finally, we analyze how the geographic properties of a video's views evolve on a daily basis during its lifetime, providing new insights on how the geographic reach of a video changes as its popularity peaks and then fades away. Our results demonstrate how, despite the global nature of the Web, online video consumption appears constrained by geographic locality of interest: this has a potential impact on a wide range of systems and applications, spanning from delivery networks to recommendation and discovery engines, providing new directions for future research. 
2. Abstract of Cited Paper (BIB002):  YouTube is the most popular service in today's Internet. Google relies on its massive content delivery network (CDN) to push YouTube videos as close as possible to the end-users, both to improve their watching experience as well as to reduce the load on the core of the network, using dynamic server selection strategies. However, we show that such a dynamic approach can actually have negative effects on the end-user quality of experience (QoE). Through the comprehensive analysis of one month of YouTube flow traces collected at the network of a large European ISP, we report a real case study in which YouTube QoE-relevant degradation affecting a large number of users occurs as a result of Google's server selection strategies. We present an iterative and structured process to detect, characterize, and diagnose QoE-relevant anomalies in CDN distributed services such as YouTube. The overall process uses statistical analysis methodologies to unveil the root causes behind automatically detected problems linked to the dynamics of CDNs' server selection strategies. 
3. Abstract of Cited Paper (BIB003):  Popular online video-on-demand (VoD) services all maintain a large catalog of videos for their users to access. The knowledge of video popularity is very important for system operation , such as video caching on content distribution network (CDN) servers. The video popularity distribution at a given time is quite well understood. We study how the video popularity changes with time, for different types of videos, and apply the results to design video caching strategies. Our study is based on analyzing the video access levels over time, based on data provided by a large video service provider. Our main finding is, while there are variations, the glory days of a video’s popularity typically pass by quickly and the probability of replaying a video by the same user is low. The reason appears to be due to fairly regular number of users and view time per day for each user, and continuous arrival of new videos. All these facts will affect how video popularity changes, hence also affect the optimal video caching strategy. Based on the observation from our measurement study, we propose a mixed replication strategy (of LFU and FIFO) that can handle different kinds of videos. Offline strategy assuming tomorrow’s video popularity is known in advance is used as a performance benchmark. Through trace-driven simulation, we show that the caching performance achieved by the mixed strategy is very close to the performance achieved by the offline strategy.

Literature Review:
End-user behavior and OSN relationships are not easy to be objectively characterized since they are dependent on various external factors and intrinsic human behavior and psychology. However, it is imperative to understand these traits since they can greatly increase the efficiency of CP algorithms in CCDNs  . End-users have a short attention span and limited time to devout to watching videos. For instance, an end-user may suffer from constrained eyeball BIB004 , which restricts the time he can watch a video. Typically, 85% of end-users watch videos for less than 90 seconds BIB003 , with a low probability of replay, whether it is due to the end-users' lack of interest or poor quality of experience (QoE) BIB004 . Such external factors complicate an accurate popularity prediction, which is essential for CP algorithms in CCDNs. Apart from the general end-users' actions (e.g., play, forward and/or reverse playback positions of video content), end-users can change the resolution of playback and the mode of playback screen. These features are offered by various VoD and UGC service providers, such as Netflix and YouTube. The resolutions range from standard, high or ultrahigh definition and the mode of playback screen is either standard or full screen. Generally, VoD and UGC applications playback a video under default resolution and mode settings that the end-users rarely and only slightly change BIB001 . More recently, VoD and UGC providers have expanded their applications and services by offering Dynamic Adaptive Streaming over HTTP (DASH). This is in contrast to the traditional, fixed, constant quality video streaming mechanism. In DASH, different resolutions are used within a single video playback using many different video bitrates. In case of YouTube, the bitrates are dynamically adapted to varying bandwidths BIB003 . For YouTube, DASH is increasingly becoming the default option set by end-users since they enjoy near optimal viewing experience with DASH BIB003 . Though DASH achieves high QoE, its dynamic adaptation complicates CP algorithms for CCDNs. Other end-user characteristics BIB004 and VoD content relationships include the relationship of end-user viewing time and view duration, the replay probability and subscription and the end-user membership and its effect on replay probability. As the number of videos the end-user accesses increases, the duration of the videos selected decreases. Users with higher number of view counts tend to have a lower replay probability and they seldom replay the same videos many times. Instead, many different videos may be replayed by a user each for a few times. However, the overall probability of replay is very low. Interestingly, active users tend to have a higher replay percentage. That is, when a user is actively subscribed to a service, there is a higher probability that the user is interested in content and hence the replay probability is higher. OSN also plays a vital role in aggregating the view count of a UGV. End-users post links to UGVs (their own or those made publically available by others) that are posted on the UGC service providers' website. Service providers (e.g., YouTube) record the number of views from the referral videos and sites. Social sharing BIB002 is view counts that are attributed to end-users accessing UGC by clicking on a 100 VOLUME  2018 link from an OSN website or application. Fig. 10 illustrates the impact of OSN on the lifecycle of videos hosted in the UGC catalogue. An average of about one third of the view counts for a YouTube video is based on social sharing BIB002 . UGV and OSN relationships are complex. Although UGV benefits greatly from social sharing as its popularity rises, social sharing becomes less influential in raising the view count of UGVs BIB002 . Content can enjoy global or local popularity with respect to geographic regions. OSN relationships can influence this geographic spread in a non-trivial manner, that is, too much or too little social sharing restricts the geographic region of UGVs. However, for hot UGVs, there is no correlation between global popularity and social sharing.