Video Streaming Research Articles

The Influence of Product Quality and Price to Resubscribe Intention through Customer Satisfaction as a Mediating Variable on SVoD Amazon Prime Video

This study aims to explore the influence of product quality and price on resubscribe intention with customer satisfaction as a mediating variable in SVoD service, namely Amazon Prime Video. This study applied a quantitative approach with a data collection method in the form of a survey with a questionnaire as the research instrument. The population in this study are people who live in Jabodetabek and have used or are currently using the Amazon Prime Video streaming service. The analysis technique used in this study is the SEM-PLS (Partial Least Squares) method with a calculating tool, namely SmartPLS version 4, to assess validity, reliability, and test research hypotheses. The results of this research show that: 1) product quality influences customer satisfaction, 2) price influences customer satisfaction, 3) product quality influences resubscribe intention, 4) price influences resubscribe intention, 5) customer satisfaction influences resubscribe intention, 6) product quality influences resubscribe intention, mediated by customer satisfaction, and 7) price influences resubscribe intention, mediated by customer satisfaction. This study is expected to contribute to developing knowledge about customer behavior, especially for researchers who are interested in analyzing the relationship and influence of product quality and price on resubscribe intention through customer satisfaction. Keyword: quality product, price, resubscribe intention, customer satisfaction, subscription video on demand.

Usability, Ergonomics, and Educational Value of a Novel Telestration Tool for Surgical Coaching: Usability Study.

Telementoring studies found technical challenges in achieving accurate and stable annotations during live surgery using commercially available telestration software intraoperatively. To address the gap, a wireless handheld telestration device was developed to facilitate dynamic user interaction with live video streams. This study aims to find the perceived usability, ergonomics, and educational value of a first-generation handheld wireless telestration platform. A prototype was developed with four core hand-held functions: (1) free-hand annotation, (2) cursor navigation, (3) overlay and manipulation (rotation) of ghost (avatar) instrumentation, and (4) hand-held video feed navigation on a remote monitor. This device uses a proprietary augmented reality platform. Surgeons and trainees were invited to test the core functions of the platform by performing standardized tasks. Usability and ergonomics were evaluated with a validated system usability scale and a 5-point Likert scale survey, which also evaluated the perceived educational value of the device. In total, 10 people (9 surgeons and 1 senior resident; 5 male and 5 female) participated. Participants strongly agreed or agreed (SA/A) that it was easy to perform annotations (SA/A 9, 90% and neutral 0, 0%), video feed navigation (SA/A 8, 80% and neutral 1, 10%), and manipulation of ghost (avatar) instruments on the monitor (SA/A 6, 60% and neutral 3, 30%). Regarding ergonomics, 40% (4) of participants agreed or strongly agreed (neutral 4, 40%) that the device was physically comfortable to use and hold. These results are consistent with open-ended comments on the device's size and weight. The average system usability scale was 70 (SD 12.5; median 75, IQR 63-84) indicating an above average usability score. Participants responded favorably to the device's perceived educational value, particularly for postoperative coaching (agree 6, 60%, strongly agree 4, 40%). This study presents the preliminary usability results of a novel first-generation telestration tool customized for use in surgical coaching. Favorable usability and perceived educational value were reported. Future iterations of the device should focus on incorporating user feedback and additional studies should be conducted to evaluate its effectiveness for improving surgical education. Ultimately, such tools can be incorporated into pedagogical models of surgical coaching to optimize feedback and training.

DeCa360: Deadline-aware edge caching for two-tier 360° video streaming

Two-tier 360° video streaming provides a robust solution for handling inaccurate viewport prediction and varying network conditions. Within this paradigm, the client employs a dual-buffer mechanism consisting of a long buffer for panoramic basic-quality segments and a short buffer for high-quality tiles. However, designing an efficient edge caching strategy for two-tier 360° videos is non-trivial. First, as basic-quality segments and high-quality tiles possess different delivery deadlines as well as content popularity, ignoring these discrepancies may result in inefficient edge caching. Second, accurately predicting the popularity of 360° videos at a fine granularity of video segments and tiles remains a challenge. To address these issues, we present DeCa360, a deadline-aware edge caching framework for 360° videos. Specifically, we introduce a lightweight runtime cache partitioning approach to achieve a careful balance between improving the cache hit ratio and guaranteeing more on-time delivery of objects. Moreover, we design a content popularity prediction method for two-tier 360° videos that combines a learning-based prediction model with domain knowledge of video streaming, leading to improved prediction accuracy and efficient cache replacement. Extensive experimental evaluations demonstrate that DeCa360 outperforms all baseline algorithms in terms of byte-hit ratio and on-time delivery ratio, making it a promising approach for efficient edge caching of 360° videos.

Factors That Influence Binge Watching Behavior

This research aims to determine the influence of enjoyment, efficiency, and escape on binge-watching behavior. This study uses a quantitative method with a correlational approach to identify the relationships between variables without the influence of extraneous variables. The subjects of this research are consumers who subscribe to OTT video streaming services and engage in watching activities for more than 2 hours in one sitting. The sample size used in this study is 142 respondents with data analysis techniques using SEM- PLS. The results indicate a positive and significant relationship between enjoyment, efficiency, and escape with binge-watching behavior.

Approach to Video Stream Quality Requirements Justification for FPV Control of Unmanned Systems

Currently, hybrid orbital-ground communication networks play a key role in various sectors of the Russian economy, an important part of which are unmanned systems based on FPV control. One of the key elements of such systems is information exchange channels. The main criterion for the success of their objectives is to meet the requirements for the quality of the transmitted video stream, which makes it particularly relevant to substantiate these requirements. The purpose of this article is to present an approach to the quantitative justification of the requirements for the quality of the transmitted video stream. The essence of the presented approach lies in the fact that the required values of the transmitted image quality indicators, used to justify acceptable neural network codecs, are determined by analyzing various video streams, on the basis of which the impacts on the control of unmanned systems that allowed to achieve the goals of their functioning were formed. The main stages of the approach and their logical interrelation are considered. The proposed approach is based on the use of methods of statistical and risk analysis, the theory of experiment planning and probability theory.Scientific novelty of the proposed approach lies in the fact that the requirements to the quality of the video stream transmitted from the unmanned system to the operator, used to justify acceptable neural network codecs, are calculated by analyzing the totality of video streams that allowed to achieve the goals of its functioning, which allows to get rid of the subjectivism inherent in the expert methods currently used to solve this problem.Theoretical significance of the proposed approach lies in the fact that the necessity and possibility of not only substantiating the requirements for the quality indicators of the transmitted video stream for FPV control, but also their correction has been proved. Practical significance. The requirements for the quality indicators of the transmitted video stream obtained using the proposed approach can be further used to justify optimal design solutions for creating information exchange channels between unmanned systems and the operator, which will significantly increase the efficiency of using these systems.

Streaming Video Service Repertoire: Patterns, Drivers, and Decisions

ABSTRACT As subscription video-on-demand (SVOD) competition heats up with more newcomers beyond the quintessential Netflix, examining streaming video consumption from the perspective of an SVOD repertoire (a set of service subscriptions by a streaming user) can help the industry understand how consumers make decisions in forming their SVOD collection and their corresponding subscription patterns for marketing insights. Existing repertoire and SVOD studies still focus on users’ content selections and preferences, and the mechanisms and drivers affecting customer choices, without realizing that consumers are not seeking to pick the best SVOD out of all. Instead, consumers are attempting to curate an exquisite selection of services that satisfy their content needs for a reasonable price while avoiding streaming confusion (uncertainty about where to access the content) and fatigue (too many options). This study explored consumers’ SVOD repertoire within the U.S. context, investigating the SVOD repertoire patterns (size and composition) and differences in the SVOD repertoires regarding consumers’ demographics, media ecosystem, streaming behavior, and perceived repertoire adequacy through an online survey method. Through an analysis of responses from 1038 participants, the study demonstrated the value of the repertoire approach in examining SVOD subscriptions. Results showed the dynamic importance of certain SVOD services and configuration changes of one’s repertoire according to size. Netflix (as the likely core), Amazon Prime Video, and Hulu (as the two most likely complements) were the most subscribed SVODs. Differences were found in demographics, media ecosystem, streaming behavior, and perceived repertoire adequacy of users across different SVOD repertoire types.

A Digital Twin of the Trondheim Fjord for Environmental Monitoring—A Pilot Case

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period.

A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X).

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous (“Go”/”No-Go”) zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.

A computer vision–aided methodology for bridge flexibility identification from ambient vibrations

AbstractThis paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: and . The first scale factor related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor . Finally, the flexibility estimated from the ambient vibration are scaled by and , respectively to obtain the exact flexibility , which are same as the analytical ones . Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real‐life bridge's condition assessment.

5G‐based video summarization: An analysis from a methodological point of view

AbstractSurveillance is one of the fast‐growing applications used for monitoring and watching people, objects, or the environment to collect information and provide security. The surveillance data is in video form, and analyzing large video is challenging because it is essential to do efficient video streaming online. Video summarization comprises selecting, extracting, and aggregating keyframes for creating a synopsis, which is challenging. Though several methods have been proposed for video summarization, most are inconsistent, poor in processing and delivering video content, and do not focus on solving the root problems interlinked with efficient streaming. Thus, video streaming applications require an efficient video summarization model that can overcome existing issues and challenges and improve the overall quality of service integrated with the advanced technology of 5G. This paper has aimed to discuss various methods, approaches, and technologies used for video summarization to design a better model. It also presents various learning models and a taxonomy of available methods and provides a detailed review. The summary of the model used evaluates its outcome and the existing methods for potential future research works. The proposed approach is compared with existing ones to prove the model's efficiency. The result shows that the proposed model achieved a 62.3 and 52.3 F1 score summarizing the TVSum and SumMe datasets, respectively.

Reform of Photography Education in Chinese Applied Universities in the Self-Media Era: Evidence from Responsible Persons in the Photography Major

Live streaming and short videos have become the most favored means of communication, and the prevalence of self-media has had a profound impact on traditional media industries, indirectly promoting reforms in Chinese photography education. This study delves into the current state of photography education in Chinese applied universities and the transformations undertaken in response to the challenges posed by self-media. The research covers the development status of university photography programs, the impact of live streaming and short videos on photography education, changes in teaching models, and the issues encountered during these reforms. Through in-depth interviews with heads and instructors of photography programs at five applied universities in China, and using NVIVO software for thematic analysis, the study examines the professional divisions, curriculum design, teaching methods, student employment rates, reform policies, and the challenges faced. The research finds that the prevalence of live streaming and short videos has had a positive impact on the dynamic imaging specialties within photography education, but has been detrimental to the static imaging specialties. Universities have actively reformed their teaching methods in response to market demands, yet still face challenges in integrating live streaming-related courses. This study provides the latest insights for practitioners in the Chinese self-media industry and offers valuable references for university policymakers and researchers.

6-Degree Vision based Tracking of a Mandible Phantom with Deep Learning

Abstract During maxillofacial surgery, the precise placement of surgical tools is crucial for accurate implant placement. Particularly if multiple implants are needed, e.g., after cancerous bone removal, visual landmarks might not be obtainable. To this end, we propose a vision-based tracking approach with deep learning. Our markerless tracking approach is based on video streams from two cameras for tracking a mandible phantom. We study, to what extent vision-based localization using deep learning is feasible. For real-time 6D pose estimation we propose a Siamese network with a ResNet-18 subnetwork. We acquire a large training dataset with a robot and evaluate the tracking accuracy on partially occluded images. Thereby, we mimic visual information that is accessible during clinical interventions. We report a mean position error of 1.33 ± 1.14mm and a rotation error of 0.86 ± 0.71 deg for partially occluded images. Overall we present a promising tracking approach that is marker-free and robust toward image artifacts.

Toward High-Quality Low-Latency 360° Video Streaming With Edge–Client Collaborative Caching and Super-Resolution

Toward High-Quality Low-Latency 360° Video Streaming With Edge–Client Collaborative Caching and Super-Resolution

Cluster-based cooperative fog caching for scalable coded videos of multiple content providers

Efficient content caching in video streaming services is important for improving user experience as well as reducing network bandwidth consumption. Fog caching combined with scalable video coding (SVC) has the potential to significantly improve caching efficiency. However, challenges such as the limited storage capacity of fog nodes and determining the optimal number of SVC layers must be overcome for their effective adoption. This becomes more complicated with multiple content providers requiring shared cache resources. To the best of our knowledge, no existing research has simultaneously tackled all these aspects. In this paper, we present Cluster-based Cooperative Fog Caching (CCo-Fog), a holistic caching strategy that enables multiple content providers to share the scarce storage of fog nodes in a multi-tier fog network to judiciously cache SVC videos in a cooperative manner. In particular, CCo-Fog consists of a cluster-based storage partitioning method and tier-wise cooperative content placement policies. The partitioning method distributes the storage of each fog node to multiple content providers for users clustered based on their population density and their proximity to fog nodes. The content placement policies determine the optimal number of SVC layers of each video for different tiers of the fog network by solving a latency-aware content placement optimization problem. Our evaluations on a real-world dataset and various configurations demonstrate the efficacy of CCo-Fog, showing a reduction in latency by about 60% and an increase in fog hit ratio by about 20% on average, compared to state-of-the-art caching strategies.

Digital Twin-Based Alternate Ego Modeling and Simulation: Eva Herzigová as a 3D MetaHuman Avatar

Abstract Due to Dimension Studios and Unsigned Group, by likeness and movement digitization, the human supermodel Eva Herzigová’s highly realistic metahuman and 3D digital twin lead to perpetual career extension (e.g., on virtual runway shows by catwalk replication or in advertising campaigns by particular pose adoption), including in virtual worlds such as metaverse spaces, by use of extended reality technologies, in terms of virtual clothing, digital hair, skin texture, facial and body expressions, and makeup styling as avatar appearance and persona. In this paper, we show how, by likeness capturing (e.g., signature walk by motion capture shoot for face and body movement tracking) and virtual clothing, Eva Herzigová’s hyperreal 3D avatar (lifelike 3D digital human clone or realistic virtual human) is configured with subsequent restyled hair and make-up, 3D clothing modeling, and extended reality fashion shows on virtual catwalks or campaign shoots. We clarify that photographic reference materials and a sensor-based motion capture suit assisted Metahuman Creator in capturing facial and motion data for Eva Herzigová’s digital avatar, configuring realistic depiction and virtual activations of beauty standards, in addition to digital likeness, signature style, talent representation, and narrations, typifying digital human creation and presence in virtual worlds (e.g., for metaverse-based shoots). We conclude that Eva Herzigová’s hyperrealistic 3D digital human twin and MetaHuman avatar operate as a styled virtual alter ego and versatile personal brand extension by motion capture technologies for immersive virtual fashion shows, live video streams and broadcast, and digital wearable modeling.

Pengaruh Self Congruity, Perceived Price, Brand Trust, dan Customer Engagement terhadap Willingness to Continue and Subscribe pada Disney Plus Hotstar

The advancement of online streaming media and the growing interest of the Indonesian population in Video on Demand (VoD) applications have paralleled the development of the internet in Indonesia. The presence of various video streaming platforms with local, regional, and global reach operating in Indonesia has generated significant dynamics in the digital entertainment industry. This phenomenon has rapidly transformed the landscape, fostering the adoption of technology and altering consumer behavior patterns in accessing entertainment content. This research is aimed at uncovering the relationship between self-congruity and perceived price through various variables such as customer engagement and brand trust, which have implications on willingness to continue and subscribe. The study employs a quantitative research method or survey approach. Sample selection is conducted through non-probability sampling using purposive sampling techniques, with a total sample of 146 respondents who are customers of Disney+ Hotstar Indonesia. Data analysis is performed using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach. The findings of the research reveal positive and significant influences, namely, self-congruity on brand trust, self-congruity on customer engagement, perceived price on customer engagement, brand trust on willingness to continue and subscribe, and customer engagement on willingness to continue and subscribe.

Learning Accurate Network Dynamics for Enhanced Adaptive Video Streaming

Learning Accurate Network Dynamics for Enhanced Adaptive Video Streaming

A new approach to video summary generation

This paper describes a video summary approach for an actual video file that provides a rapid way to assess its information. This is a difficult task to identify relevant or informative sections. When the original video demands knowledge of its content, we build a video summary via extracting in-depth characteristics of each original video. Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information provides a concise and accurate representation of the original video clips by providing the most representative summary with increased interest through the application of a matching function between the current and prior frames. The primary goal of this work is to give a strategy for recording video clips using security cameras and converting them into a short video to provide a clear analysis of the video by reducing excess and extracting the key video contents into the real video. In a lengthy video clip, viewers may not have enough time to see the full thing. The viewer may be interested in seeing a specific topic under the label "Important". It may be composed of pieces that may be shared on social media and communicate the semantics of long videos, particularly video snippets that must be submitted to Internet. To browse, will be needed a network with adequate bandwidth. That is, non-affiliated video streaming, indexing, and retrieval of video content makes it valuable to viewers.

A UAV-Centric Improved Soft Actor-Critic Algorithm for QoE-Focused Aerial Video Streaming

A UAV-Centric Improved Soft Actor-Critic Algorithm for QoE-Focused Aerial Video Streaming