• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.706-718
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• 2018

This study analyzed Naver TV users' pattern of video clip watching, and analyzed the factors affecting the popularity of Naver TV's video clip. We selected 572 individual video clips that were ranked 50th in Naver TV rankings from September 10th to September 24th in 2017. We classified video clip's characteristics into several factors, including the number of likes, the number of subscriber, genre, video clip's types, and star appearances. We indexed the popularity of video clip, which implies the degree of popularity for each video clip. The results showed that the number of likes for video clips and the number of subscribers for each video clip were positively related to the popularity of video clip. Video clip's genre, video clip's type and star power positively affected the popularity of video clip. The effect of extras genre on the popularity of video clip was the lowest, followed by entertainment, music, and drama genre. but the difference among entertainment, music and drama genre was not statistically significant. Web-only video and non-broadcast video positively affected the popularity of video clip. Finally, the popularity of video clip was higher when stars appeared in the video clip.

• Journal of the Korean Home Economics Association
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• v.40 no.9
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• pp.35-46
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• 2002

This study was to investigate whether there are any differences in social competence by the frequency of young children's video and computer game use. Social development was categorized as peer popularity and social competence. The subjects were 215 children(118 boys, 97 girls) aged 4-6 years(M= 63.6 months, SD=6.8) from 3 kindergartens in Chung-Cheong Nam Do. The frequency of children's video and computer game use was reported by their parents. Peer popularity was rated by their classmates and social competence by their teachers with Kohn Social Competence Scale(KSCS). No significant relationship was found between game use and peer popularity. The children who played video and computer games once or twice a week got the highest score on the‘social interest and participation’But social cooperation dimension was not related with the frequency of video and computer game use but with the sex of children.

• Asia pacific journal of information systems
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• v.33 no.2
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• pp.282-297
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• 2023

One of the emerging trends in the marketing field is digital video marketing. Online videos offer rich content typically containing more information than any other type of content (e.g., audible or textual content). Accordingly, previous researchers have examined factors influencing videos' popularity. However, few studies have examined what causes a video to remain popular. Some videos achieve continuous, ongoing popularity, while others fade out quickly. For practitioners, videos at the recommendation slots may serve as strong communication channels, as many potential consumers are exposed to such videos. So,this study will provide practitioners important advice regarding how to choose videos that will survive as long-lasting favorites, allowing them to advertise in a cost-effective manner. Using deep learning techniques, this study extracts text from videos and measured the videos' tones, including factual and emotional tones. Additionally, we measure the aesthetic score by analyzing the thumbnail images in the data. We then empirically show that the cognitive features of a video, such as the tone of a message and the aesthetic assessment of a thumbnail image, play an important role in determining videos' long-term popularity. We believe that this is the first study of its kind to examine new factors that aid in ensuring a video remains popular using both deep learning and econometric methodologies.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.621-628
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• 2000

A retrieval performance of VOD sever is estimated by how quickly it services popular videos to users and how many users it is able to service. Each video data is placed on heterogeneous disks and placement techniques are various, retrieval performance is under the control of these elements, so that a retrieval performance is affected by placement policy. In this paper, we place video data considering their characteristics, especially, we place videos distributively according to their popularity. To verify our policy, we make various environment of experiment, estimate a placement policy using popularity of videos and a contrary policy, and compare them.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.147-153
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• 2011

In this paper, we present a new video management technique using forward timeline to efficiently store and delete the videos on a local server. The proposed method is based on capturing the changing preference of the videos according to recentness, frequency, and playback length of the requested videos. For this purpose, we utilize the forward timeline which represents the time area within a number of predefined intervals. The local server periodically measures time popularity and request segment of all videos. Based on the measured data, time popularity and request segment, the local server calculates the mean time popularity and mean request segment of a video using forward timeline. Using mean time popularity and mean request segment of video, we estimate the ranking and allocated storage space of a video. The ranking represents the priority of deletion when the storage area of local server is running out of space and the allocated storage space means the maximum size of storage space to be allocated to a video. In addition, we propose an efficient storage space partitioning technique in order to stably store videos and present a time based free-up storage space technique using the expected variation of video data in order for avoiding the overflow on a local server in advance. The simulation results show that the proposed method performs better than other methods in terms of hit rate and number of deletion. Therefore, our video management technique for local server provides the lowest user start-up latency and the highest bandwidth saving significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3635-3654
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• 2015

With the rapid development of smart devices and mobile Internet, the video application plays an increasingly important role on mobile devices. Understanding user behavior patterns is critical for optimized operation of mobile live streaming systems. On the other hand, volume based billing models on cloud services make it easier for video service providers to scale their services as well as to reduce the waste from oversized service capacities. In this paper, the watching behaviors of a commercial mobile live streaming system are studied in a content-centric manner. Our analysis captures the intrinsic correlation existing between popularity and watching intensity of programs due to the synchronized watching behaviors with program schedule. The watching pattern is further used to estimate traffic volume generated by the program, which is useful on data volume capacity reservation and billing strategy selection in cloud services. The traffic range of programs is estimated based on a naive popularity prediction. In cross validation, the traffic ranges of around 94% of programs are successfully estimated. In high popularity programs (>20000 viewers), the overestimated traffic is less than 15% of real happened traffic when using upper bound to estimate program traffic.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.41-54
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• 2007

The recent UCC video sector possesses an explosive potential for growth as it will act as a critical factor in determining the popularity of portal sites. We identified and compared the strengths and weaknesses in the UCC sector of "Naver" and "Daum", which are currently in the development phase, and we investigated alternatives as well as proposing an outlook for both portal companies. We believe a better UCC video service will be the deciding factor in a portal's popularity and therefore researches in the UCC video sector must continue.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.4
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• pp.138-144
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• 2007

With recent proliferation of video-on-demand services, caching in a multimedia streaming server is becoming increasingly important. Previous studies have shown that request interval based caching and its extension for considering different video popularity performs well in various streaming environments. In this paper, we show that block level refinement of this existing scheme can further improve the performance of streaming servers. Trace driven simulations with real world VOD traces have shown that the proposed scheme improves the cache hit rate and the startup latency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1498-1507
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• 2017

According to recent report, global mobile data traffic is expected to increase by 11 times from 2016 to 2020. Moreover, this growth is expected to be driven mainly by mobile video traffic which is expected to account for about 70% of the total mobile data traffic. To cope with enormous mobile traffic, we need to understand video traffic's characteristic. Recently, the repetitive requests of some popular content such as popular YouTube videos cause a enormous network traffic overheads. If we constitute a network with the nodes capable of content caching based on the content popularity, we can reduce the network overheads by using the cached content for every request. Through device-to-device, multicast, and helpers, the video throughput can improve about 1.5~2 times and prefix caching reduces the playback delay by about 0.2~0.5 times than the conventional method. In this paper, we introduce some recent work on content popularity-based caching techniques in wireless networks.