Streaming Video Content Research Articles

A Study on Subscribers Perception towards Over The Top (OTT) Platform

Over-the-Top (OTT) video streaming services through national and international providers has changed consumer behavior towards entertainment subscription. The online screening and streaming video content providing platforms which can also be said as OTT (Over-The-Top) services providing platforms have successfully marked their presence in the entertainment part of people`s life in India. In this view the first objectives aims to explore how different demographic factor such as age, gender, income, and education, impact consumer experiences with OTT subscriptions. By analyzing data on subscriber satisfaction, content preferences, and usage patterns, the study will identify trends and insights that can help OTT providers tailor their services to better meet the needs of diverse user groups. Lastly this study focused on understanding subscribers’ attitudes and behaviors regarding the usage of OTT platforms within a specific geographic or demographic context. Overall, while users express satisfaction with various aspects of OTT services, particularly regarding security, content variety, and value for money, there are key areas for improvement, particularly in usability and flexibility. Addressing these issues could further enhance the overall user or OTT viewers experience in Mysore City.

Video stalling identification for web live streaming under HTTP-FLV

Video stalling identification for web live streaming under HTTP-FLV

An Interactive Multimedia Model for Developing QOE-Enhanced E-Learning Platforms

This paper provides an approach to providing QOE enhancement for a multifaceted learning platform that includes streamed media content. We demonstrate how the QOE of a multifaceted platform can be enhanced using the Lighthouse tool. The Lighthouse tool helped improve the page load speed by 75%. We also demonstrate how the playback quality of the video streaming content can be enhanced using CMAF-formatted DASH content. When compared to single-bit rate media, DASH-based media provided better video playback quality. The study also evaluates the learners` preferences for specific interactive features of a learning platform. The least preferred feature uniquely had static content. The results of this study provide empirical data that would help instructional designers make feature selection choices that`s guided by empirical data and improve the QOE of their instructional learning platforms.

A decision model for OTT service users to adopt wireless D2D caching networks: Exploring the Korean case

A decision model for OTT service users to adopt wireless D2D caching networks: Exploring the Korean case

The State of Streaming Video Content at Academic Libraries

This paper provides a literature review of academic library collection activities related to the provision of streaming video content in support of higher education curricula. It reviews the budgetary, collection management, licensing, technology, and acquisition processes and associated challenges that the provision of streaming video content poses for academic libraries in offering this much needed content to their patron base. The authors present a recent history of developing academic video collections, the transition to and increased demand for the streaming video format, and the evaluations required of funding models and vendor supply models to make the availability of streaming video content sustainable in the academic environment. The article also reviews current and future research and planned collaboration which hold great promise in addressing the current challenges academic libraries face in offering streaming video content to their patrons.

Redirection and Protocol Mechanisms in Content Delivery Network-Edge Servers for Adaptive Video Streaming

Redirection and protocol techniques are key components of the infrastructure for Content Delivery Networks (CDNs) that aid in the delivery of Multimedia Internet services to end-consumers. Redirection methods are used to route the user’s request to the nearest edge server, minimizing distance, improving delivery times, and lowering latency. Protocol mechanisms, such as HTTP Live Streaming (HLS), Dynamic Adaptive Streaming over HTTP (DASH), and Real-Time Messaging Protocol (RTMP), are used to deliver adaptive video streaming. These protocols are designed to transfer the adaptive streaming and provide high-quality video playback. They also allow the system to adjust the video quality based on the network conditions of the Quality of Service (QoS). Inadequate transmission protocols and poorly designed redirection algorithms are two major challenges that might degrade Cloud–CDN performance. These challenges lead to excessive latency, poor quality of service, and significant packet loss that have potential influences on the user experience. In this paper, firstly, three protocols are proposed by preparing a case study on selecting the optimal protocol for replicating adaptive video streaming content. Secondly, a redirection algorithm based on the Modified Cuckoo Search Algorithm (MCSA) is proposed to provide an accurate redirecting process of selected edge servers to end-users. Test results indicate that, when hybrid FASP/HTTP protocols were chosen (from original server to replicate server and to end-users), the delivery of adaptive video streaming segments was fast with lower latency. The average estimated time needed for replicating video content based on FASP is 25% better than that needed for File catalyst and Signiant protocols. Therefore, the Cuckoo search method presents more efficient results for selecting the optimal edge server for 100 servers, which is 0.296 s, compared to the conventional algorithm, which was 13 s.

The Impact of e-Wom on Users’ Attitudes Toward Over-the-top (Ott) Streaming Video Content and its Subscription Intention. – Young Indians Perspective

Purpose: Empirical research suggests a high impact of e-WOM on the user’s attitude leading to their behavioral intention. This research paper is a novel attempt to understand the impact of electronic word-of-mouth on users’ attitudes toward the content of OTT, leading to its subscription intention. Theoretical framework: “Content” is one of the key factors behind the exponential growth and viewership of OTT platforms in India. But how the users develop their attitude to watch or subscribe a specific content largely depends on word of mouth (WOM) or electronic word of mouth(e-WOM) information. Design/method/approach: The data is collected from 250 respondents using the measured items from existing scales to test their validity and reliability in the OTT industry. Structural equation modeling is then used to examine the influence of e-WOM on the user’s attitudes toward content and subscription intention. Findings: The results indicate a positive and significant impact of e-WOM on the user’s attitude towards content and subscription intention. e-WOM is a significant predictor Research, Practical & Social implication: The paper offers valuable insights to the prominent marketers of OTTs in devising appropriate content promotion or content enhancement strategies which could lead to a favorable e-WOM about their content. Originality/value: There has been immense research on the different aspects OTTs, but none has been done in context to OTT streaming video content’s e-WOM and subscription intention. Thus, the paper extends the existing research on OTTs to a new area, namely e-WOM.

AN ANALYSIS OF STUDENT’S RESILIENCE IN ONLINE ENGLISH CLASS DURING PANDEMIC ERA AT ENGLISH STUDY PROGRAM UNIVERSITAS MUHAMMADIYAH BENGKULU

The COVID-19 pandemic has changed the way universities learn. Teaching and learning activities that usually take place face-to-face have evolved into virtual meetings in various online learning applications. This paper aims to analyze the resilience of students in handling online English courses from a perspective or from the perspective of coping with online learning challenges during the COVID-19 pandemic. This document collects 20 students who are actively participating in online learning activities as an example for the "Teaching English as a Foreign Language" course. Online learning offers a variety of teacher-led instruction. Instructions can be synchronous (communications where participants interact simultaneously, such as video conferencing, Zoom, Google Meet, and WebEx) or asynchronous (time-separated communications, such as email, Google Forms, streaming video content, posting handouts) and social media platforms). This study adopts a qualitative method. The researchers collected, read and highlighted each student's responses that were deemed relevant to the analysis. This article gives many answers to the challenges students face in online learning such as: skills, finding online learning media.

Machine Learning for Per-Title Encoding

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Video streaming content varies in terms of complexity and requires title-specific encoding settings to achieve a certain visual quality. Classic “one-size-fits-all” encoding ladders ignore video-specific characteristics and apply the same encoding settings across all video files. In the worst-case scenario, this approach can lead to quality impairments, encoding artifacts, or unnecessarily large media files. A per-title encoding solution has the potential to significantly decrease the storage and delivery costs of video streams while improving the perceptual quality of the video. Conventional per-title encoding solutions typically require a large number of test encodes, resulting in high computational times and costs. In this article, we describe a solution that implements the conventional per-title encoding approach and uses its resulting data for machine learning-based improvements. By applying supervised, multivariate regression algorithms like random forest regression, multilayer perceptron (MLP), and support vector regression, we can predict video quality metric (VMAF) values. These video quality metric values are the foundation for deriving the optimal encoding ladder. As a result, the test encodes are eliminated while preserving the benefits of conventional per-title encoding</i> .

A Study on Over-the-Top (OTT) Video Streaming Platforms in Bangladesh

This paper aims to identify the opportunities and challenges of over-the-top (OTT) video streaming platforms in Bangladesh. Furthermore, the study aims to reveal the viewership patterns of Bangladeshi consumers regarding different OTT platforms, such as Netflix, Chorki, Hoichoi, and Prime Video. The popularity of over-the-top (OTT) video streaming is on the rise worldwide. Bangladesh is becoming an emerging field of business in the global OTT market due to the proliferation and rapid development of internet services and smartphones. No academic research has been conducted on this rapidly emerging form of entertainment in Bangladesh, in contrast to mainstream entertainment sources such as satellite television and film. It aims to uncover the current state of this new medium, its prospects, and its regulatory challenges. This study is the first to examine the current scenario of over-the-top (OTT) video streaming platforms in Bangladesh. Bangladeshi viewers’ preferences and prospects for OTT platforms are assessed in this study. Investigating consumption patterns and the central tendency regarding OTT content and media was conducted in Bangladesh. Using the convenience sampling technique, an online survey collected feedback from 100 respondents (N = 100). A majority of OTT subscribers (67%) agree that OTT is their primary source of entertainment. Additionally, 69% of OTT subscribers in Bangladesh believe the future of OTT video streaming is bright. Watching OTT video streaming content is most popular on smartphones. Therefore, 66 percent of OTT users want to continue their subscriptions. According to this study, Netflix is the viewers’ most popular OTT video streaming service.

An Optimized Bandwidth Estimation for Adaptive Video Streaming Systems Using WLBWO Algorithm

The increasing popularity of streaming video is a cause of concern for the stability of the internet because most streaming video content is currently delivered via UDP without any end-to-end congestion control. Since the internet relies on end systems implementing transmit rate regulation, there has recently been significant interest in congestion control mechanisms that are both fair to TCP and effective in delivering real-time streams. Streaming video over the internet requires dealing with bandwidth and delay that vary over time. Many video streaming applications address this problem by adapting the quality of the scalable video. But it produces poor quality service, and sending data on this channel results in buffering time. To trounce these issues, this paper proposed optimized bandwidth estimation for adaptive video streaming systems using the WLBWO algorithm. Originally, the input video is compressed by using the UHE algorithm. Next, the system proposes a KEECC to securely transfer the data. Then, the encrypted data is sent to the receiver via a multipath channel. Before sending the data to the receiver, the bandwidth is estimated by using the WLBWO. Finally, the inverse process is performed. Extensive experimental results showed the effectiveness of the proposed system than conventional methods.

Evaluating Kanopy Access Models in Academic Libraries: Balancing Demand and Budget Constraints

Many academic libraries make streaming video content available to their users on the Kanopy platform. This article explores how libraries have approached Kanopy’s various access models, of which the most common are a patron driven acquisition (PDA) access model and a mediated access model. The author examines her institution’s switch in models in light of budgetary concerns and concludes that the change to mediate access has not led to significant delays in providing access to requested materials.

Alternative media framing of COVID-19 risks

Early media coverage of COVID-19, between 1 January and 31 March 2020, provided Alternative Media Personalities (AMPs) an opportunity to provide conspiratorial misinformation to their online audiences. Far-right AMPs may reframe sociopolitical aspects of risk to produce ‘fake-news’, amplifying future risks arising from the COVID-19 pandemic. Using the Social Amplification of Risk Framework (SARF) to define factors of risk amplification, this study conducted a framing analysis upon 1,895 minutes of streamed video content from a popular, far-right, AMP regarding COVID-19. Significant differences in frame expression suggested that AMPs hold greater value in specific frames when providing infotainment based upon authentic interpretations of risk. A lack of significant change in frame expression over time suggests that AMPs may rely upon media templates when communicating risk to their audience. Qualitative data suggest that different aspects of risk amplification work in concert to provide discursive contexts for far-right AMPs to define risks from their ideological standpoint. The data provided by this study better outline some of the complexities facing scientific communications strategies which seek to directly address misinformation online.

A Case Study on Real-time Live Video Streaming Content

With the development of new media, great changes are taking place in the way people get information. The change is the use of video content that can deliver content in a more three-dimensional way than words or photos. After 2016, the number of live video streaming content providers and users has increased. In this paper the write takes the 1 personal live video streaming content as the research object. And the write takes live video streaming content on YouTube live or Douyu TV as a research example. In this paper, the writer analyzes the digital information content in the live video streaming case. And the writer expounds the necessity of these visual information and the characteristics of real-time live video streaming content. Especially since 2020, because of the influence of the COVID-19, the live video streaming industry has begun to combine with the traditional industry. It is expected that the integration of digital cutting-edge technology and live video streaming will not only provide diversity in the content, but also create more social value for the video content consumption culture. Therefore, The writer thinks it is necessary to conduct in-depth research on the social responsibility of real-time live content in the future.

Associative Knowledge Graph Using Fuzzy Clustering and Min-Max Normalization in Video Contents

Video content data have a variety of objects that could be associated with each other. Although content data contains similar objects or themes, their associations can become ambiguous. Accordingly, if associations between video content data are found in general association rules, their accuracy and confidence are low. Therefore, this study proposes the associative knowledge graph using fuzzy clustering and min-max normalization in video contents. With the use of the objects of video content, the proposed method finds clear and accurate associations between video content data and generates a knowledge graph. In the first step, the streaming video content data massively generated are collected, and objects in each image video are classified by an object detection algorithm. In the second step, normalization is executed in consideration of the different length of each video content and generate transaction data based on object frequency. In this way, it is possible to consider all the collected video content in the same condition. Additionally, it is possible to find unnecessary objects and significant objects in video content. Lastly, the degree of ambiguity is analyzed through fuzzy clustering using a probability that each object is involved in a group. Associations between fuzzy-clustered objects are extracted and an association knowledge graph is created. In this way, the accuracy and confidence of associations between different video content data are improved. As for performance, the association knowledge graph generated in the proposed method was better than a conventional association rule method in terms of the count of generated rules, support, and confidence.

Listening to Music “in” the Library

Digital and streaming audio and video (A/V) content have usurped the primacy of physical media materials and their playback technology within institutional music libraries, notwithstanding throwbacks to and resurgences of physical media in commercial and personal contexts. Music libraries are challenged with the conflicting responsibilities of maintaining legacy format materials that are not digitally available, continuing to collect physical resources that are not available either digitally or through institutional streaming subscriptions, and acquiring born-digital and digitized resources. They must also reconcile these responsibilities with the fact that many streaming A/V resources are freely available to individual consumers. In an era of dwindling resources and appreciation for curated music collections, how will libraries ensure that their A/V resources, in all formats, remain relevant to current and future users? This paper presents the results of an A/V usage survey administered to affiliates of the University of Alberta’s Music Department to learn about the attitudes, preferences and experiences of music library users’ practises for accessing recorded music.

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

Multipath MMT-based approach for streaming high quality video over multiple wireless access networks

Demand for video streaming content and multimedia services have increased dramatically. Delivering high throughput and low delay video data are real challenges to provide and sustain high quality mobile video streaming services. One type of solution to provide quality of experience is to exploit multipath strategies by taking advantage of the multiple network interfaces that are currently available in most wireless devices. Our work aims at delivering contributions by exploring the advantages of multipath video streaming using the MPEG Media Transport protocol. MPEG Media Transport is a multimedia application layer protocol with the capability of hybrid media delivery. In this article, we propose a Content-Aware and Path-Aware (CAPA) scheduling strategy for this protocol, considering both video features and path condition. The performance of our CAPA proposal is evaluated using ns-3 DCE with different realistic multipath network scenarios. We evaluate the performance of CAPA over heterogeneous wireless networks under congested network and wireless lossy network conditions, which are common network situations with significant adverse effects on video quality. Our approach yields considerable video quality improvement in both scenarios compared to Path-Aware strategy and a simple scheduling strategy, called Evenly Splitting. For congested network scenario, CAPA could increase PSNR, respectively, by up to 4.25 dB (12.97%), and 7.22 dB (20.58%) compared to Path-Aware and Evenly Splitting strategies. It could also improve SSIM, respectively, by up to 0.033 (3.78%), and 0.102 (12.54%) compared to Path-Aware and Evenly Splitting strategies. For wireless lossy network scenario, CAPA increases PSNR, respectively, by up to 6.84 dB (20.30%), and 9.43 dB (30.32%) compared to Path-Aware and Evenly Splitting strategies. The proposed strategy also provides improvements in terms of SSIM, by up to 0.100 (12.72%), and 0.113 (14.23%) compared to Path-Aware and Evenly Splitting strategies, respectively. We also evaluate how proposed CAPA handles the streaming of different video bitrates under congested network conditions to provide sufficient video quality. Finally, we provide an initial validation of fairness of CAPA to confirm fair access to the resources available across all paths.

Entropy-Driven Adaptive Filtering for High-Accuracy and Resource-Efficient FPGA-Based Neural Network Systems

Binarized neural networks are well suited for FPGA accelerators since their fine-grained architecture allows the creation of custom operators to support low-precision arithmetic operations, and the reduction in memory requirements means that all the network parameters can be stored in internal memory. Although good progress has been made to improve the accuracy of binarized networks, it can be significantly lower than networks where weights and activations have multi-bit precision. In this paper, we address this issue by adaptively choosing the number of frames used during inference, exploiting the high frame rates that binarized neural networks can achieve. We present a novel entropy-based adaptive filtering technique that improves accuracy by varying the system’s processing rate based on the entropy present in the neural network output. We focus on using real data captured with a standard camera rather than using standard datasets that do not realistically represent the artifacts in video stream content. The overall design has been prototyped on the Avnet Zedboard, which achieved 70.4% accuracy with a full processing pipeline from video capture to final classification output, which is 1.9 times better compared to the base static frame rate system. The main feature of the system is that while the classification rate averages a constant 30 fps, the real processing rate is dynamic and varies between 30 and 142 fps, adapting to the complexity of the data. The dynamic processing rate results in better efficiency that simply working at full frame rate while delivering high accuracy.

Update from the World Wide Web Consortium (WC3)

Within the World Wide Web Consortium (W3C), the Media and Entertainment activity both tracks and standardizes the media-related topics and features needed to create immersive experiences for end users. HTML5, WebVTT, TTML, and TTML profiles brought standard audio, video, and captions to the Web. Standardization activities since then have turned the Web into a professional platform fully suitable for the delivery of media content and associated materials by enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix, and Google, W3C received a Technology and Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web.