A Video Timeline with Bookmarks and Prefetch State for Faster Video Browsing

Carlier, Aurélie; Charvillat, Vincent; Ooi, Wei Tsang

doi:10.1145/2733373.2806376

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Although not particularly for an educational context, Video timelines with bookmarks allows smooth pre-fetching and the indication of buffering has been proposed (Carlier et al, 2015); Ice hockey goal events are highlighted and shown as mini icons on the timeline of the playback UI (Müller et al, 2010). Many experimental video retrieval systems in the field of multimedia (e.g., the annual Video Browser Showdown showcase; Schoeffmann, 2019) have been experimenting with more sophisticated and advanced within-video highlights where the retrieval unit is sub-video parts (e.g., a frame, camera shot, scene, etc.)…”

Section: Within-video Recommendation and Timeline Visualizationmentioning

confidence: 99%

Playback-centric visualizations of video usage using weighted interactions to guide where to watch in an educational context

et al. 2022

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The steady increase in use of online educational tools and services has led to a large amount of educational video materials made available for students to watch. Finding the right video content is usually supported by the overarching learning management system and its user interface that organizes various video items by course, categories and weeks, and makes them searchable. However, once a wanted video is found, students are often left without further guidance as to what parts in that video they should focus on. In this article, an additional timeline visualization to augment the conventional playback timeline is introduced which employs a novel playback weighting strategy in which the history of different video interactions generate scores based on the context of each playback. This includes whether the playback started after jumping forward or backward in the video, whether the playback was at a faster or slower speed, and whether the playback window was in focus on the student's screen or was in the background. The resultant scores are presented on the additional timeline, making it in effect a playback-centric usage graph nuanced by how each playback was executed. Students are informed by this visualization on the playback by their peers and can selectively watch those portions which the contour of the usage visualization suggests. The visualization was implemented as a fully-fledged web application and deployed in an undergraduate course at a university for two full semesters. A total of 270 students used the system throughout both semesters watching 52 videos, guided by visualizations on what to watch. Analysis of playback logs revealed that students selectively watched portions in videos corresponding to the most important portions of the videos as assessed by the instructor who created the videos. The characteristics of this method as a way of guiding students as to where to watch as well as a complementary tool for playback analysis, are discussed. Further insights into the potential values of this visualization and its underlying playback weighting strategy are also discussed.

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Section: Within-video Recommendation and Timeline Visualizationmentioning

confidence: 99%

Playback-centric visualizations of video usage using weighted interactions to guide where to watch in an educational context

et al. 2022

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Section: Playback-centric Visualisations Of Video Usagementioning

confidence: 99%

Playback-centric visualisations of video usage using weighted interactions to guide where to watch in an educational context

Lee,

Liu,

Scriney

et al. 2022

Preprint

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The steady increase in use of online educational tools and services has led to a large amount of educational video materials made available for students to watch. Finding the right video content is usually supported by the overarching learning management system and its user interface that organises various video items by course, categories and weeks, and makes them searchable. However, once a wanted video is found, students are often left without further guidance as to what parts in that video they should focus on.In this article, an additional timeline visualisation to augment the conventional playback timeline is introduced which employs a novel playback weighting strategy in which the history of different video interactions generate scores based on the context of each playback. This includes whether the playback started after jumping forward or backward in the video, whether the playback was at a faster or slower speed, and whether the playback window was in focus on the student's screen or was in the background. The resultant scores are presented on the additional timeline, making it in effect a playback-centric usage graph nuanced by how each playback was executed. Students are informed by this visualisation on the playback by their peers and can selectively watch those portions which the contour of the usage visualisation suggests.The visualisation was implemented as a fully-fledged web application and deployed in an undergraduate course at a university for two full semesters. A total of 270 students used the system throughout both semesters watching 52 videos, guided by visualisations on what to 1

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“…2) User Behavior Model: We model users' transition behavior among series in the same TV series as a Markov model based on the insights learned from the measurement study in Sec. II-C. As opposed to the short videos published on social video sharing sites where users browser videos quickly [9], users tend to complete watching episodes of TV series [8]. We set the duration of each time slot the same value for each episode, thus the user only watches one video in one time slot.…”

Section: B System Assumptions 1) Content Modelmentioning

confidence: 99%

Toward Wi-Fi AP-Assisted Content Prefetching for an On-Demand TV Series: A Learning-Based Approach

Jin

Wen

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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The emergence of smart Wi-Fi APs (Access Point), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (QoE) (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching, which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users, and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real-traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov Decision Process (MDP). The objective is to strike a balance between the increased prefetching&storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first, we investigate the performance lower bound by using a random algorithm, and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm.Index Terms-Wi-Fi AP, content prefetching, learning-based approach.

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A Video Timeline with Bookmarks and Prefetch State for Faster Video Browsing

Cited by 8 publications

References 18 publications

Playback-centric visualizations of video usage using weighted interactions to guide where to watch in an educational context

Playback-centric visualizations of video usage using weighted interactions to guide where to watch in an educational context

Playback-centric visualisations of video usage using weighted interactions to guide where to watch in an educational context

Toward Wi-Fi AP-Assisted Content Prefetching for an On-Demand TV Series: A Learning-Based Approach

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