Video Delivery Research Articles

HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution

In recent years, 360-degree videos have gained significant traction due to their capacity to provide immersive experiences. However, the adoption of 360-degree videos substantially escalates bandwidth demands, necessitating approximately four to ten times more bandwidth than traditional video formats do. This presents a considerable challenge in maintaining high-quality videos in environments characterized by limited bandwidth or unstable networks. A trend has emerged where client-side computational power and deep neural networks are employed to enhance video quality while mitigating bandwidth requirements within contemporary video delivery systems. These approaches segment a video into discrete chunks and apply super resolution (SR) models to each segment, streaming low-resolution (LR) chunks alongside their corresponding SR models to the client. Although these methods enhance both video quality and transmission efficiency for conventional videos, they impose greater computational resource demands when applied to 360-degree content, thereby constraining widespread implementation. This paper introduces an innovative method called HVASR for 360-degree videos that leverages viewport information for more precise segmentation and minimizes model training costs as well as bandwidth requirements. Additionally, HVASR incorporates a viewport-aware training strategy that is aimed at further enhancing performance while reducing computational expenses. The experimental results demonstrate that HVASR achieves an average utility increase ranging from 12.46% to 40.89% across various scenes.

The Evolution of Volumetric Video: A Survey of Smart Transcoding and Compression Approaches

Volumetric video, the capture and display of three-dimensional (3D) imagery, has emerged as a revolutionary technology poised to transform the media landscape, enabling immersive experiences that transcend the limitations of traditional 2D video. One of the key challenges in this domain is the efficient delivery of these high-bandwidth, data-intensive volumetric video streams, which requires innovative transcoding and compression techniques. This research paper explores the state-of-the-art in volumetric video compression and delivery, with a focus on the potential of AI-driven solutions to address the unique challenges posed by this emerging medium.

The Careful Project: Evaluating the acceptability of local virtual reality experiences as a leisure activity for residents in aged care.

The aim of this project was to evaluate the feasibility and acceptability of virtual reality (VR) as a leisure activity for people living in residential aged care. Virtual reality experiences may offer alternatives for residents with limited mobility, or during times when older people are unable to leave residential care. The intervention used VR videos delivered via a head-mounted device. Video content was created by a local artist specifically for use with older Western Australian adults and used local content. VR sessions were videotaped for analysis. The evaluation included structured observation of video content to assess mood and engagement and post-intervention interviews with participants. Mood and engagement scores indicated overall favourable mood and considerable positive engagement with the VR experience. Interview content analysis reflected the enjoyment participants took in visiting places they had once been and the surprising ways that the experience made them feel and act as though they were actually there. Videos featuring coastal visits were the most popular and videos featuring music were the most engaging. Some participants found the head-mounted device uncomfortable. VR was considered potentially beneficial for residents with limited mobility or living with cognitive impairment. Overall VR is a feasible and acceptable leisure activity for older people in residential care. The use of local content has the potential to prompt memories and generate new conversations. Future use of VR may consider different modes of video delivery and advancements in evaluation.

IoT Video Delivery Optimization through Machine Learning-Based Frame Resolution Adjustment

Providing acceptable video quality in the Internet of Things (IoT) implementation poses a significant challenge, mainly when the application is performed on low-cost and low-power devices. This research focuses on developing a frame resolution adjustment system that maintains the frame rate value of video delivery in wireless IoT environments with resource-constrained devices. Consistent frame rates prevent motion lag and data loss, improving user experience. The system works by predicting the upcoming throughput values using machine learning methods to adjust the sensing parameter, which is the resolution of the video frame to be captured by camera nodes. Hence, the proposed system is equipped with a file size estimator to estimate the size of the next video frame and then adjust the resolution in accordance with the throughput prediction. In this research, we conducted extensive experiments to evaluate the accuracy of the file size estimator and the throughput prediction. The experiment generated a dataset to evaluate throughput prediction and file size estimator model. The evaluation results for the file size estimator showed a mean absolute percentage error (MAPE) of 6.73% in the experiment using 317 frames with video resolutions between 72p and 720p. Experiments were also conducted to compare several machine learning methods for predicting throughput values. Compared to long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES) outperforms the others with the lowest root mean squared error (RMSE) and mean absolute error (MAE) values. Building upon these findings, we implemented the frame resolution adjustment system using SES as the method for predicting the upcoming throughput values. Finally, we demonstrated that the proposed system can maintain the frame rate according to the threshold set by the system while the resolution is being maximized, thereby addressing the challenges of maintaining video quality in resource-constrained IoT environments.

Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.

Application of the integrated data platform combined with dietary management for adults with diabetes: A prospective randomized controlled trial.

To investigate the efficacy of the integrated data platform of cloud hospital combined with dietary management for adults with type 2 diabetes. We conducted a randomized controlled clinical trial. One hundred eighty patients with type 2 diabetes were randomly allocated into a control group (Group A) and an experimental group (Group B). Routine standard diabetes care was applied to the patients in Group A. The integrated data platform with dietary management was applied to Group B. Individualized diabetes education videos were sent to the patients through the platform. The primary endpoint was the change in HbA1c and change in body weight from baseline to Week 12 during the follow-up. At Week 12, HbA1c was 7.4 ± 0.7%, 6.9 ± 0.9% in Groups A and B, P < 0.01. The rate of fasting blood glucose <7 mmol/L, and glycosylated hemoglobin <7% was higher in Group B than in Group A. At Week 12, there was a significant weight loss and body mass index decrease in the overweight or obese patients of the experimental group. Those overweight or obese patients in the experimental group utilizing the appetite suppressant semaglutide achieved the most significant weight loss, with a 13.4% reduction after 12 weeks. The integrated data platform combined with personalized diabetes education video delivery was verified to be a more effective management mode for diabetes. For overweight or obese adults with diabetes, the use of semaglutide in conjunction with dietary management and the integrated data platform led to greater weight loss.

Smart Media Video Cloud Technology in News Communication

Cloud technology revolutionizes the way businesses and individuals store, access, and manage data and applications. It involves the use of remote servers hosted on the internet to store and process information, providing on-demand computing resources. With the cloud, users can access their data and applications from anywhere with an internet connection, fostering increased collaboration, flexibility, and scalability. This research explores the convergence of smart media video cloud technology and news communication, introducing a novel framework named Big Data Analytics Parallel Edge Computing with ECC (PEC-ECC). With the proliferation of video content in news dissemination, there is a growing need for innovative technologies to enhance efficiency, security, and accessibility. PEC-ECC integrates the power of big data analytics, parallel edge computing, and error-correcting code mechanisms to optimize video processing and delivery. This framework not only addresses the challenges of data volume and computational speed but also ensures the integrity and security of transmitted content. By leveraging edge computing capabilities, PEC-ECC minimizes latency, making real-time news communication more responsive and reliable. This research contributes to the advancement of smart media technology, offering a robust solution to elevate the quality and effectiveness of video-based news communication in the era of digital journalism.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Super-resolution with perceptual quality for improved live streaming delivery on edge computing

According to Cisco projections, mobile connection throughput is anticipated to more than triple by the year 2023, with the expected typical 5G connection throughput reaching 575 Mbps. Additionally, smartphones and TV sets equipped with computational capabilities for decoding 4K videos have significantly redefined the landscape of Internet video traffic, accounting for 82% of the total Internet traffic. This surge in connection throughput, coupled with the escalating demand for video-centric content, presents an unprecedented challenge in effectively managing traffic across the backhaul infrastructure of 5G networks. In this article, we introduce and evaluate the On-edge enhanced live streaming with super-resolution (ELiveSR) framework for live video delivery, which takes advantage of throughput improvements, in the access networks, and makes use of real-time super-resolution procedure to reduce transmission rate demands in the backhaul segment of the network. This SR procedure is carried by a deep neural network based model and is capable of coping with video content of different subjects. Experimental assessments conducted revealed that the proposed framework can reduce video-related traffic in the backhaul segment by up to 88.37%. Additionally, it simultaneously enhances the quality of experience metrics observed during live video streaming sessions.

Perceptions of the acceptability and effectiveness of a localised lifestyle medicine initiative to improve mental health – the lift project

ABSTRACT Objective Evaluate survey respondents’ perceptions concerning the acceptability and effectiveness of a localised, community-based mental health program. Method Avondale University and Lake Macquarie City Council partnered to implement an online, lifestyle-based program for all Council residents during a COVID-19 lockdown period. The University and Council promoted the program through established networks, resulting in 2390 registrations representing 4041 participants. Researchers invited registered users to respond to a post-program survey assessing their perceptions concerning program satisfaction, mental health benefits, and self-efficacy for managing mental health. Qualitative questions probed respondents’ likes and dislikes and were analysed thematically. Results Response rates were low, 6% (138/2390) of registered participants completed the post-program survey. Of the 138 responses, 99% indicated they would recommend the program to others, and 94% believed they gained skills to manage their mental well-being into the future. Themes generated from the qualitative data indicated that respondents liked the video content and delivery style, community camaraderie, personal empowerment, and easy accessibility. Some respondents found online accessibility problematic, disliked the delivery style, and experienced technical difficulties. Discussion The findings from a limited number of respondents indicate that a localised, community-based lifestyle program may offer a replicable model that is acceptable and effective in improving perceived mental health and self-efficacy in managing mental well-being. Survey respondents valued the practical content, community togetherness and emphasis on self-empowerment.

Edge caching and computing of video chunks in multi-tier wireless networks

This paper proposes a video caching and transcoding strategy for delay-constrained content delivery in multi-tier wireless networks. Particularly for multimedia services whose content can be encoded into multiple quality versions and consists of multiple chunks, we present an approach of caching chunks of an identical file separately in different network layers with different qualities. Given this caching policy, a complete file can be retrieved by receiving its chunks from different network tiers; therefore, a video transcoding and delivery method is jointly designed with caching to maximize the expected content quality with a strict delay constraint. Motivated by that caching is proactively conducted in advance of many user requests, the joint optimization problem is decomposed into caching and transcoding subproblems. The optimal transcoding policy and its transcoded qualities at different network tiers are analytically found by linear programming for a given cache state. Using the closed-form transcoded qualities, the caching policy is optimized to maximize the expected content quality with respect to randomness of user locations and channel gains. Due to the difficulty of expectation, the caching policy of each network layer is independently obtained by water-filling first, and an alternating optimization method is used to find the joint optimal caching policy satisfying the cache size constraints. Simulation results show that the proposed scheme succeeds in delivering content of 5% to 10% higher expected quality to users than comparison schemes which do not use partitioning for caching. Simultaneously, our approach meets the delay requirement and effectively manages the trade-offs arising from the varying storage and computational capacities within hierarchies, as well as video diversity.

An empirical assessment of the use of an algorithm factory for video delivery operations.

Video service providers are moving from focusing on Quality of Service (QoS) to Quality of Experience (QoE) in their video networks since the users' demand for high-quality video content is continually growing. By focusing on QoE, video service providers can provide their subscribers with a more personalized and engaging experience, which can help increase viewer satisfaction and retention. This focus shift requires not only a more sophisticated approach to network management and new tools and technologies to measure and optimize QoE in their networks but also a novel approach to video delivery operations. This paper describes the components, interactions, and relationships of an algorithm factory for video delivery operation that assures high QoE for video streaming services. The paper also showcases the results of gradually implementing an algorithm factory in the video industry. Using a dataset from 2016 to 2022, we present the case of a European PayTV service provider that achieved improved performance measured by both objective and subjective metrics. The use of an algorithm factory significantly improved the PayTV service provider's performance. The study found a fivefold increase in the speed of critical incident resolution and a 59% reduction in the number of critical incidents, all while expanding the customer base and maintaining the same level of labor resources. The case also demonstrates a strong positive relation between the productivity measures of the PayTV operator and their survey-based quality ratings. These results underscore the importance of flawless QoS and operational excellence in delivering QoE to meet the evolving demands of viewers. The paper adds to the existing literature on relationships between operational efficiency, innovation, and subjective quality. The paper further offers empirical evidence from the PayTV industry. The insights provided are expected to benefit both traditional and over-the-top (OTT) video service providers in their quest to stay ahead in the rapidly evolving video industry. It may also translate to other service providers in similar industries committed to supporting high-quality service delivery.

A digital health intervention to support patients with chronic pain during prescription opioid tapering: a pilot randomised controlled trial.

Recent changes in opioid prescribing guidelines have led to an increasing number of patients with chronic pain being recommended to taper. However, opioid tapering can be challenging, and many patients require support. We evaluated the feasibility, acceptability, and potential efficacy of a codesigned digital health intervention to support patients with chronic pain during voluntary prescription opioid tapering. In a pilot randomised controlled trial, participants received a psychoeducational video and 28 days of text messages (2 SMS/day) in addition to their usual care (intervention) or usual care alone (control). The feasibility, acceptability, and potential efficacy of the intervention were evaluated. The primary outcome was opioid tapering self-efficacy. Secondary outcomes were pain intensity and interference, anxiety and depression symptom severity, pain catastrophising, and pain self-efficacy. Of 28 randomised participants, 26 completed the study (13 per group). Text message delivery was high (99.2%), but fidelity of video delivery was low (57.1%). Most participants rated the messages as useful, supportive, encouraging, and engaging; 78.5% would recommend the intervention to others; and 64.2% desired a longer intervention period. Tapering self-efficacy (Cohen d = 0.74) and pain self-efficacy (d = 0.41) were higher, and pain intensity (d = 0.65) and affective interference (d = 0.45) were lower in the intervention group at week 4. First evidence supports the feasibility, acceptability, and potentially efficacy of a psychoeducational video and SMS text messaging intervention to support patients with chronic pain during voluntary prescription opioid tapering. Definitive trials with longer intervention duration are warranted.

DRL Empowered On-policy and Off-policy ABR for 5G Mobile Ultra-HD Video Delivery

DRL Empowered On-policy and Off-policy ABR for 5G Mobile Ultra-HD Video Delivery

NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution

In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions. Nevertheless, deploying these solutions on mobile devices still remains an active challenge as SR models are excessively demanding with respect to workload and memory footprint. Despite recent progress on on-device SR frameworks, existing systems either penalize visual quality, lead to excessive energy consumption or make inefficient use of the available resources. This work presents NAWQ-SR, a novel framework for the efficient on-device execution of SR models. Through a novel hybrid-precision quantization technique and a runtime neural image codec, NAWQ-SR exploits the multi-precision capabilities of modern mobile NPUs in order to minimize latency, while meeting user-specified quality constraints. Moreover, NAWQ-SR selectively adapts the arithmetic precision at run time to equip the SR DNN's layers with wider representational power, improving visual quality beyond what was previously possible on NPUs. Altogether, NAWQ-SR achieves an average speedup of 7.9×, 3× and 1.91× over the state-of-the-art on-device SR systems that use heterogeneous processors (MobiSR), CPU (SplitSR) and NPU (XLSR), respectively. Furthermore, NAWQ-SR delivers an average of 3.2× speedup and 0.39 dB higher PSNR over status-quo INT8 NPU designs, but most importantly mitigates the negative effects of quantization on visual quality, setting a new state-of-the-art in the attainable quality of NPU-based SR.

Evaluating how extended reality delivery device and preservice teacher major impact presence in immersive learning environments

Teacher education has begun to embrace the use of 360 videos to improve preservice teachers' (PSTs) engagement and immersion. While recent research on such use is promising overall, there are specific questions that have been left unanswered about the construct of presence in 360 videos. More specifically, research has yet to fully explore how video delivery devices and PST characteristics may impact presence. The purpose of this study was to respond to this gap in the literature by examining PST major, delivery device (ie, head mounted display vs. flat screen), and the interaction between the two in informing presence. A total of 93 PSTs watched 360 videos and then completed a questionnaire. Findings indicated that presence in head‐mounted displays was related to emotions and agency. Conversely, presence with flat screens meant familiarity and control. Moreover, PST major was a predictor of presence, suggesting that 360 videos should be personalized according to PST career and professional goals. Finally, the interaction effect of preparing to teach K‐12 mathematics and wearing a head‐mounted display had a negative and statistically significant effect on participants' perceived presence, thus indicating a non‐cumulative effect. Practitioner notesWhat is already known about this topic 360 videos are increasingly used to support preservice teacher training. 360 videos for teacher education foster immersion, presence, and noticing skills. What this paper adds Empirical evidence that 360 videos experienced with head‐mounted display facilitate agency and emotional involvement. Empirical evidence that 360 videos experienced with flat screens facilitate feelings of control. Data showed that preservice teachers' content area (math) was a positive predictor of presence in 360 videos. Implications for practice and/or policy Head‐mounted displays show promise as a delivery mode for 360 videos in teacher education. 360 videos should be personalized according to PST career and professional goals. The eXtended Reality Presence Scale is confirmed as a robust instrument to measure presence in immersive environments. More research is needed to evaluate the scalability of this approach in other contexts.

Legislation for Video­‑Sharing Platforms on the European Audiovisual Market

The progressing convergence of television and Internet services has caused a dynamic development of the audiovisual market. The decision to regulate the subject matter of video-sharing platforms (VSPs) in the amended Audiovisual Media Services Directive (AVMSD) was dictated by recognition that such platforms compete for the same viewers and incomes as other audiovisual media services. Coordination of legislation on the European level led to the necessity to introduce amendments to Polish law. The main purpose of this article is to present selected provisions of AVMSD and Poland’s Broadcasting Act concerning VSPs as an area of media policy, which previously had fallen outside the scope of institutional intervention and regulatory restrictions relating to the traditional media market. The central question that author attempted to resolve was whether measures used in relation to VSP providers are legitimate, necessary and proportional. The study explains the definition of video delivery services which is fundamental from the point of view of imposing some obligations on these types of services.

Developing the TeamOBS-vacuum-assisted delivery checklist to assess clinical performance in a vacuum-assisted delivery: a Delphi study with initial validation.

In Northern Europe, vacuum-assisted delivery (VAD) accounts for 6-15% of all deliveries; VAD is considered safe when conducted by adequately trained personnel. However, failed vacuum extraction can be harmful to both the mother and child. Therefore, the clinical performance in VAD must be assessed to guide learning, determine a performance benchmark, and evaluate the quality to achieve an overall high performance. We were unable to identify a pre-existing tool for evaluating the clinical performance in real-life vacuum-assisted births. We aimed to develop and validate a checklist for assessing the clinical performance in VAD. We conducted a Delphi process, described as an interactive process where experts answer questions until answers converge toward a "joint opinion" (consensus). We invited international experts as Delphi panelists and reached a consensus after four Delphi rounds, described as follows: (1) the panelists were asked to add, remove, or suggest corrections to the preliminary list of items essential for evaluating clinical performance in VAD; (2) the panelists applied weights of clinical importance on a Likert scale of 1-5 for each item; (3) each panelist revised their original scores after reviewing a summary of the other panelists' scores and arguments; and (4) the TeamOBS-VAD was tested using videos of real-life VADs, and the Delphi panel made final adjustments and approved the checklist. Twelve Delphi panelists from the UK (n = 3), Norway (n = 2), Sweden (n = 3), Denmark (n = 3), and Iceland (n = 1) were included. After four Delphi rounds, the Delphi panel reached a consensus on the checklist items and scores. The TeamOBS-VAD checklist was tested using 60 videos of real-life vacuum extractions. The inter-rater agreement had an intraclass correlation coefficient (ICC) of 0.73; 95% confidence interval (95% CI) of [0.58, 0.83], and that for the average of two raters was ICC 0.84 95% CI [0.73, 0.91]. The TeamOBS-VAD score was not associated with difficulties in delivery, such as the number of contractions during vacuum extraction delivery, cephalic level, rotation, and position. Failed vacuum extraction occurred in 6% of the video deliveries, but none were associated with the teams with low clinical performance scores. The TeamOBS-VAD checklist provides a valid and reliable evaluation of the clinical performance of vaginal-assisted vacuum extraction.

Self-Help to Reduce Body-Focused Repetitive Behaviors via Video or Website? A Randomized Controlled Trial

BackgroundBody-focused repetitive behaviors (BFRBs) are common but under-recognized mental disorders. The present study aimed to examine the efficacy of a video presentation of self-help techniques for BFRBs compared to a web-based intervention containing psychoeducation and comprehensive self-help treatment information on BFRBs to reduce symptomatology as well as the same videos as in the first condition.MethodsA total of 217 participants with BFRBs were randomly assigned to (1) a video condition conveying the established self-help techniques habit reversal training (HRT), decoupling (DC), and decoupling in sensu (DC-is) to reduce BFRBs, (2) a website condition that offered psychoeducational information and treatment material, including the videos from the first treatment condition, or (3) a waitlist control (WLC) condition. A six-week post assessment was conducted. The Generic Body-Focused Repetitive Behavior Scale 45 (GBS-45) served as the primary outcome.ResultsThe self-help video condition showed greater improvement in GBS-45 subscales for nail biting in comparison to the self-help website and WLC conditions. For other BFRBs (e.g., joint cracking, trichophagia), the impairment subscale showed significant results in post hoc analyses in favor of the two treatment conditions. Subjective ratings of the techniques were satisfactory and comparable across treatment groups, with slightly higher ratings in favor of the video condition.ConclusionsThe video condition showed better improvement than the control condition on nail biting and other BFRBs. Superior results in the self-help website condition compared to the WLC were shown only for the group of “other” BFRBs (i.e., not trichotillomania, nail biting, dermatillomania, lip/cheek biting). We speculate this might be because users were overwhelmed by the many different approaches described without clear guidelines for how to implement them in daily life. In light of a prior study, we assume that a manualized version of the self-help techniques is superior to the video delivery and recommend that the self-help videos should be used as a complement to a manualized version. Future research should address long-term effects of self-help interventions for BFRBs.

Designing the video-based learning environments using workflow and scaffolding to enhance self-instructional video production ability of pre-service teacher

This study aims to design a video-based learning environment using workflow and scaffolding to enhance self-instructional video production ability of pre-service teacher and to explore the impacts of the use of the designed video-based learning environment on learning. The methodology was divided into two main phases. In the first phase, a video-based learning environment was designed using workflow and scaffolding. In the second phase, the impacts of the use of the developed video-based learning environment on learning were investigated. The samples were 47 undergraduate students at the School of Education, University of Phayao. The results were, as follows: (a) The design draft of the developed video-based learning environment was appropriate, and the revision was made according to six criteria: (1) instructional media, (2) content, (3) learning activities, (4) communication devices, (5) learning management systems, and (6) screen design. The analysis of related studies and documents, the instructional video production process consisted of three steps: (1) design, (2) development, and (3) video delivery. (b) The comparison of scores on the instructional video production knowledge test revealed that the pre-service teachers’ average post-test score was higher than the average pre-test score with a statistically significant difference at the .05 level. The assessment of instructional videos, it was found that the pre-service teachers in the field of humanities obtained a higher score than those in the field of science. Male pre-service teachers received a higher score than female pre-service teachers. The average suit-ability score indicated the overall appropriateness of the developed video-based learning environment.