Video Content Providers Research Articles

Consumer Behavior in Adopting Useetv Go Streaming & Video-On-Demand Services Using Unified Theory Of Acceptance And Use Of Technology 2 (UTAUT2)

The increase in online activities is followed by increased spending to meet online needs, one of which is entertainment services. One of the most widely accessed entertainment facilities is digital TV and video-on-demand or VoD services. The service, which began to flourish in 2016, is an online video content provider system with a subscription payment mechanism. One of its appeals, the user has the freedom to decide what he wants to see. One way to entertain yourself which has become a new lifestyle pattern is to watch streaming shows or subscription videos online. A McKinsey Company survey at the end of March said that as many as 45% of respondents spent more on home entertainment during the pandemic. On the other hand, 85% of respondents reduced their spending on outdoor entertainment. Subscription video streaming services (video-on-demand / VoD) are one of the entertainment options that can be done at home. UseeTV GO is an Over-the-Top (OTT) TV and Video mobile application service, using the publicly accessible Internet. The content is available via smartphones or tablets with broadband connections. Currently, UseeTV Go app users are around 300 thousand active users every month, far below Vidio.com and Netflix. With the low Monthly Active of UseeTV Go's Video-on-demand app users in the pre-growth area far below, Netflix, Vidio.com, and user activeness based on time, UseeTV Go is in the Average Performer area. Therefore, UseeTV Go consumer assessment of the variables contained in the modified UTAUT2 model (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Condition, Hedonic Motivation, Price Value, Habit, and Content) was carried out on Behavioural Intention on UseeTV Go video-on-demand service in Indonesia.

Priority-Aware Resource Allocation for 5G mmWave Multicast Broadcast Services

5G Multicast Broadcast Services (MBS) are viewed as a promising 5G New Radio (NR) application, as standardization begins in 3GPP Release 17. With MBS, one next generation Node B (gNB) delivers data to multiple user equipments (UE) simultaneously, thus improving spectrum efficiency. Millimeter wave (mmWave) beamforming further enhances system performance by focusing signals in a dedicated direction. However, despite the advantages, we identify three issues of multicast with beamforming techniques. First, link directionality causes the gNB to transmit data over the beams sequentially, resulting in a combinatorial resource allocation problem. Confronting this beam scheduling issue, we develop an optimization algorithm that obtains an optimal solution in polynomial time. Second, UEs may falsely report their valuations over beam resources to gain more utility. Under this scenario, the gNB cannot allocate the resources to those in need due to the lack of accurate UE information. Therefore, we propose a Vickrey–Clarke–Groves (VCG) auction-based mechanism to incentivize the UEs to reveal their valuations over resources truthfully. This mechanism guarantees solution efficiency and maximizes social welfare. Third, as 3GPP standards allow for different priorities for different multicast flows, and video content providers distinguish between ordinary and premium UEs, we take UE priority into account. In this regard, we extend the valuation-based mechanism to a multi-priority one. Finally, the mathematical analysis validates some desirable properties of the proposed scheme, such as incentive-compatibility. Simulation results also justify our superior performance in the 5G MBS system compared with other resource allocation schemes.

Lightweight Video Super-Resolution for Compressed Video

Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.

Pemanfaatan Media Sosial Instagram Akun @Mahasiswaakhir.Id Sebagai Media Literasi Informasi Mahasiswa

This study aims to see how the use of social media Instagram account @mahasiswaakhir.id as a medium for information literacy in students. This study uses qualitative methods with participatory observation data collection techniques and interviews. The research results show that the @mahasiswaakhir.id account is an Instagram account that seeks to become an information literacy medium, especially for students by providing relevant information in the form of video tips and tutorials as well as motivational content that can help and benefit students. This shows that the @mahasiswaakhir.id account utilizes Instagram social media as an information literacy medium for students by providing video content information, tutorial tips and motivation to students so that students can choose and process information according to their needs. The conclusion of this study is that social media has been utilized as well as possible to disseminate information.
 
 Keywords: Account @mahasiswaakhir.id, Information literacy, Instagram, Students,

Net Neutrality and Online Innovation: An Empirical Study of the UK

Abstract How does a fast lane on the internet affect the entry of online content providers? To answer this question, I provide empirical evidence that connection speed greatly influences consumption of online video and that changes in speed therefore directly impact the entry of video content providers. I also present a framework to think about the incentives of broadband providers to offer a fast lane with access fees which has direct profits effects as well as indirect demand effects. Finally, by combining the empirical model with the theoretical framework in a counterfactual analysis, I show that a fast lane with access fees only increases entry for content providers with high fixed costs of content provision. Using a novel data set on household consumption of online content and broadband penetration in local markets to estimate a structural model of industry demand and supply, this paper is the first to provide empirical evidence on the industry-wide effects of breaking net neutrality.

Development and analysis of models for service migration to the MEC server based on hysteresis approach

Online video services are among the most popular ways of content consumption. Video hosting servers have a very high load every day, which we propose to reduce by migrating the application with the video content in demand to the local Multi-access Edge Computing (MEC) server of the target. This makes it possible to improve the quality of services (QoS) provided to users by reducing the transmission delay. Therefore, an architecture has been proposed that allows, at times of increased demand for the same video content, to migrate the video service application to the edge servers of the network operator. To evaluate the performance of this approach, a mathematical model was developed in the form of a queuing system. The results of the numerical experiment make it possible to optimize the time of using local MEC servers to provide video content.

A Collaborative Task Offloading Framework for Smart TV Applications in a Household Computing Environment

Smart TV can perform interactive computing while also providing video content services. However, this leads to a high delay during interactive computing because of the lack of computing capability, thus smart TVs are unable to undertake large scenes and complex interactive computing tasks. This article proposes a collaborative task offloading framework (CTOF) for interactive computing of smart TV video applications. The main contributions of this article are as follows: 1) a computing offloading mechanism is proposed for interactive computing with video content. A part of the interactive computing task is offloaded to the user’s high-computing mobile device in a household video service environment via a Wi-Fi Direct channel and 2) a collaborative computing offloading algorithm is proposed for complex interactive computing tasks. According to the computing complexity and the computing expansion coefficient, a parallel and serial collaborative smart offloading framework is established to minimize the delay. We conduct extensive experiments to indicate that in the existing experimental network environment, the video-based complex interactive service operation efficiency is improved by 20%. With a smart offloading framework, we can further achieve a satisfactory experience in terms of the interactive computing delay. Consequently, the smart TV can quickly respond to the complex video interactive service and improve the business interaction capability of the smart TV.

Analysis of MP4 Videos in 5G Using SDN

Current video content providers require a huge demand for network resources to obtain the best quality of experience from the end-users perspective. This measure can be achieved if the network can anticipate itself to different existing issues (bandwidth problems, etc.). Due to traditional Internet Protocol networks cannot handle this problem due to the rigidity of its architecture, software defined networks is introduced as a potential answer for resolving the existing challenges in video content delivery decoupling the control plane and data plane. Utilizing the unified perspective of the network, it is possible to develop a monitoring tool obtaining the network metrics to be used afterwards. In this paper, we propose the application of different clustering algorithms for monitoring software defined networks to validate the number of statistics queries decrements using the data and error rates to improve the network traffic and to reduce the overload. The simulations demonstrate that the expectation-maximization algorithm highly reduces the monitoring queries maintaining the best data and error rate values in the video delivery.

Feature‐based no‐reference video quality assessment using Extra Trees

With the emergence of social networks and improvements in the internet speed, the video data has become an ever-increasing portion of the global internet traffic. Besides the content, the quality of a video sequence is an important issue at the user end which is often affected by various factors such as compression. Therefore, monitoring the quality is crucial for the video content and service providers. A simple monitoring approach is to compare the raw video content (uncompressed) with the received data at the receiver. In most practical scenarios, however, the reference video sequence is not available. Consequently, it is desirable to have a general reference-less method for assessing the perceived quality of any given video sequence. In this paper, a no-reference video quality assessment technique based on video features is proposed. In particular, a long list of video features (21 sets of features, each consisting of 1 to 216 features) is considered and all possible combinations ( 2 21 − 1 ${2^{21}-1}$ ) for training an Extra Trees regressor is examined. This choice of the regressor is wisely selected and is observed to perform better than other common regressors. The results reveal that the top 20 performing feature subsets all outperform the existing feature-based assessment methods in terms of the Pearson linear correlation coefficient (PLCC) or the Spearman rank order correlation coefficient (SROCC). Specially, the best performing regressor achieves PLCC = 0.786 ${\text{ PLCC}=0.786}$ on the test data over the KonVid-1k dataset. It is believed that the results of the comprehensive comparison could be potentially useful for other feature-based video-related problems. The source codes of the implementations are publicly available.

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

Стратегічна взаємодія провайдерів диференційованих інтернет-послуг

По мірі того, як обчислення та Інтернет-з’єднання стають технологіями і послугами загального призначення, спрямованими на широкі глобальні ринки, виникають питання ефективності таких ринків з точки зору суспільного добробуту, участі в них провайдерів диференційованих послуг і кінцевих користувачів. Проект Iridium глобального зв’язку фірми Motorola завершився у 1990-х роках внаслідок подібних питань, вперше досягши мети технологічної спроможності такого зв’язку. Оскільки Інтернет-послуги характеризуються високою інноваційністю, диференційованістю та динамічністю, то для них можна застосовувати відомі моделі диференційованих продуктів. Проте функції попиту у таких моделях є гіперболічними, а не лінійними. Крім того, подібні моделі стохастичні та включають провайдерів з різними способами конкуренції. В екосистемі Інтернету важливими є зв’язки між постачальниками Інтернет-послуг (Internet service providers — ISPs) як операторами телекомунікаційної мережі та провайдерами послуг контенту, особливо потужними провайдерами відеоконтенту, що передбачає високу пропускну здатність. Оскільки підвищення пропускної здатності потребує нових інвестицій у спроможність мережі, то мотивація до таких інвестицій має бути як у провайдерів відеоконтенту, так і у ISPs. Для аналізу взаємозв’язків між провайдерами Інтернет-послуг і контент-провайдерами в Інтернет-екосистемі запропоновано обчислювані (computable) моделі, основані на побудові функцій виграшу всіх учасників цієї екосистеми. Введення платного перегляду контенту мотивуватиме провайдерів Інтернет-послуг до інвестицій у підвищення спроможностей мережі, яка має тренд експоненціального зростання. Водночас такий перегляд порушуватиме принципи нейтральності мережі, що дає підстави для розробки нових задач мінімізації порушень умов нейтральності мережі та максимізації суспільного добробуту Інтернет-екосистеми. Моделі вказують на важливість ефективності провайдерів Інтернет-послуг, передбачуваності попиту та високої цінової еластичності інноваційних послуг.

The YouTube Social Network

Today, YouTube is the largest user-driven video content provider in the world; it has become a major platform for disseminating multimedia information. A major contribution to its success comes from the user-to-user social experience that differentiates it from traditional content broadcasters. This work examines the social network aspect of YouTube by measuring the full-scale YouTube subscription graph, comment graph, and video content corpus. We find YouTube to deviate significantly from network characteristics that mark traditional online social networks, such as homophily, reciprocative linking, and assortativity. However, comparing to reported characteristics of another content-driven online social network, Twitter, YouTube is remarkably similar. Examining the social and content facets of user popularity, we find a stronger correlation between a user's social popularity and his/her most popular content as opposed to typical content popularity. Finally, we demonstrate an application of our measurements for classifying YouTube Partners, who are selected users that share YouTube's advertisement revenue. Results are motivating despite the highly imbalanced nature of the classification problem.

An Explanatory Analysis on Television YouTube Industry in Sri Lanka

Social media creates an entirely different platform in the media industry. YouTube is the leading user-driven video content provider in social media, and it expands the size of viewers. Conventional television stations entered YouTube to telecast programs live and by upload in the last decade. So many stakeholders, like content creators and advertisers, engaged in the YouTube industry for different benefits. Television stations of Sri Lanka entered the YouTube platform in 2011. More than twenty Sri Lankan television stations are competitively engaged in the YouTube industry to expand their fanbase and generate new income flows. Studies on the industry are helpful to industry partners and other parties of the society. We investigate the top six television channels of Sri Lanka to explore the viewers, subscribers, expected income, and content of the programs. According to the findings, TV Derana shows the highest performance in the YouTube industry of Sri Lanka. ITN Sri Lanka shows fast growth. There is a vast gap between current performances and available opportunities in the industry. We make some recommendations to the management of television stations to gain more from this platform creatively.

Pricing mode selection for the online short video platform

Online short video platform usually adopts advertising mode and mixed mode as the pricing mode. How to choose the optimal pricing mode for the online short video platform is a topic worthy of our attention. This paper discusses the pricing mode that should be adopted by the online short video platform and investigates the effects of the viewer’s nuisance cost and cross-network externality intensity on the pricing mode selection of short video platform. The results show that, when the viewer’s nuisance cost is low and the intensity of cross-network externality is high, the platform should choose the advertising mode; otherwise, it should choose the mixed mode; when the cost is high and the intensity is low, the advertising price under the advertising mode is higher than that under the mixed mode; otherwise, the advertising price under the advertising mode is lower than that under the mixed mode; when the cost is relatively low and regardless of the intensity, the payment cost under the mixed mode is higher than that under the advertising mode; otherwise, the payment cost under the mixed mode is lower than that under the mixed mode. Importantly, we also find that, under certain conditions, interests of online short video platform and video content providers may be inconsistent.

Multicast at Edge: An Edge Network Architecture for Service-Less Crowdsourced Live Video Multicast

Using smartphones, tablets, and other portable/handheld devices, we have become more reliant on the video streaming services for entertainment and remote work. Mobile data traffic has grown eighteen folds over the past five years accounting for the majority of IP traffic. YouTube Live, Facebook Live, Twitch, DouYu and other streaming as well as video conferencing services have increased in popularity so at any given moment they serve thousands of live video streams to millions of users. The Enhanced Multimedia Broadcast Multimedia Service (eMBMS) is the standard multicast protocol for 5G networks. Cellular multicast has gained considerable attention to efficiently utilize the limited spectrum to transmit multimedia content to cellular sites with co-located viewers, lowering the cost, and maximizing the Quality of Experience (QoE). However, popular live video content providers use unicast mode for live video delivery and have limited support in the eMBMS service-oriented network architecture. In this paper, we propose an overlay network architecture to augment eMBMS to address the limitations of the standard eMBMS architecture and enable service-less multicast for crowdsourced live video providers. We propose a Virtual Network Function (VNF) service that identifies potential multicast scenarios based on user requests for a live video within a confined area. The VNF Application Server collects information, validates a potential multicast scenario, and initiates an ad-hoc multicast service on the fly. We use a real-world dataset of Facebook Live videos to evaluate the proposed architecture. The simulation results depict considerable advantages in terms of cost, efficiency, and Quality of Experience (QoE). Our results show that the proposed architecture provides significant benefits in bandwidth saving at the backhaul, transit, and RAN links.

There’s Probably a Blackout in Your Television Future: Tracking New Carriage Negotiation Strategies Between Video Content Programmers and Distributors

Video programmers and distributors execute contracts for the mutually beneficial delivery of content to consumers. However, when the parties fail to reach closure on new terms and conditions before reaching the end date of an existing agreement, cable and satellite television operators must “blackout” the content thereby triggering consumer anger and mutual financial injury. Blackouts typically have occurred infrequently and for short time periods, because both parties understand the self-inflicted financial harm these service disruptions generate. Additionally, so-called Multi-channel Video Programming Distributors (“MVPDs”) have been able to pass onto subscribers higher programming costs through increased month rates without significant declines in subscribership. The marketplace for video programming has experienced significant change in recent years calling into question the ability of MVPDs to raise rates well above annual measures of general inflation. Likewise, content distributors have experienced unprecedented declines in subscribership and consumer resentment at having to pay rates, often in excess of $50 monthly, for programming tiers containing dozens of channels, many of which few subscribers have an interest in viewing. New designations, such as cord cutting and cord shaving, refer to the increasing number of video consumers who abandon an MVPD subscription, or incur lower monthly rates by downgrading to a less expensive service tier. Video consumers also have evidenced greater interest in so-called non-linear content, available as downloadable files and streaming on demand, in lieu of conventional “live,” linear content transmitted by television broadcasters and conventional cable and satellite MVPDs. The marketplace success of Amazon Prime, Hulu and Netflix evidences growing willingness of consumers to use their broadband network access for “Over the Top” access to alternative and competitive content sources. This paper assesses whether the video marketplace has, or soon will require major changes in the terms and conditions under which content providers and distributors agree to carriage terms. The paper will explain that while MVPDs, do not operate as common carriers, such as public utilities, they bear legal rights and responsibilities, predicated on marketplace conditions necessitating regulatory support for television broadcasters. Laws and regulations by the Federal Communications Commission now appear to be based on contestable assumptions about the public interest value in promoting local content sources, diversity in programming and ownership and offering broadcasters the option of mandatory carriage by MVPDs (“must carry”) in exchange for relinquishing demands for financial compensation from MVPDs. Additionally, both traditional MVPDs and market entrants using broadband networks for content delivery, have access to ample content alternatives, many of which also can trigger blackouts if carriage agreements lapse. The paper seeks to answer whether television broadcasters have overestimated the value of their content when electing to seek compensation instead of compulsory carriage by MVPDs. The paper also will assess whether non-broadcast video content providers make similar overestimates given the greater willingness of MVPDs to tolerate more frequent and lengthy blackouts occurring throughout the year.

Controlling P2P-CDN Live Streaming Services at SDN-Enabled Multi-Access Edge Datacenters

Recognizing the shortcomings of current hybrid peer-to-peer (P2P) content-distribution network (CDN) video solutions and the potential of emerging multi-access edge datacenters, we propose a novel P2P-CDN service model that is hosted at software defined networks (SDN)-enabled multi-access edge datacenters operated by network service providers (NSP). An important feature of the proposed service architecture is that both CDN access by peers and P2P video streaming between peers within edge access networks are fully controlled by cooperation of the video content provider (VCP) and NSP to optimize video service key performance indicators (KPI). The proposed fully controlled P2P-CDN architecture with P2P group formation and chunk scheduling managed at edge datacenters reduces the load on CDN servers while overcoming quality of experience (QoE) fluctuations per flow and unfairness between multiple heterogeneous video-resolution clients over reserved access network slices. Other advantages of this service include: i) better video quality and lower delay for clients; ii) better use of edge network resources; iii) avoiding illegal, unauthorized P2P content sharing. To the best of our knowledge, there are no solutions in the literature that address P2P-CDN services managed at NSP-edge datacenters combining P2P-assisted CDN, SDN-assisted edge computing, and premium service over reserved slices. Experimental results show that the proposed P2P-CDN service deployed at SDN-enabled edge datacenters provides excellent service KPI compared to other state-of-the-art solutions.

Hybrid machine learning approach for popularity prediction of newly released contents of online video streaming services

In the industry of video content providers such as VOD and IPTV, predicting the popularity of video contents in advance is critical, not only for marketing but also for network usage. By successfully predicting user preferences, contents can be optimally deployed among servers which ultimately leads to network cost reduction. Many previous studies have predicted view-counts for this purpose. However, they normally make predictions based on historical view-count data from users, given the assumption that contents are already published to users. This can be a downside for newly released contents, which inherently does not have historical data. To address the problem, this research proposes a hybrid machine learning approach for the popularity prediction of unpublished video contents.In this paper, we propose a framework which effectively predicts the popularity of video contents, via a combination of various methods. First, we divide the entire dataset into two types, according to the characteristics of the contents. Next, the popularity prediction is performed by either using XGBoost or neural net with category embedding, which helps resolving the sparsity of categorical variables and requiring the system to learn efficiently for the specified deep neural net model. In addition, we use the FTRL model to alleviate the volatility of view-counts. Experiments are carried out with a dataset from one of the top streaming service companies, and results display overall better performance compared to various standalone methods.

PrefCache: Edge Cache Admission With User Preference Learning for Video Content Distribution

With the deployment of video streaming in 4G/5G mobile network, Content Delivery Networks (CDN) are extending to the network edge to provide end-users better Quality of Experience (QoE). However, small cache size and irregular request patterns make it a great challenge for edge caching in video content distribution. Most of the existing cache policies are item-wise, they admit each video object separately, which performs poorly on the network edge due to irregular request patterns. We observe that compared with single video objects, users’ preferences for video topics are much more constant, thus are easier to be predicted. So we propose PrefCache, a novel cache admission policy based on preference learning, for video content edge caching. PrefCache enables an edge cache to learn users’ preferences for videos in real-time. Once receiving a video object, PrefCache decides whether to admit it to the cache by whether it is under users’ preference. We make three contributions in this work. (1) First, we design an information collector, which can proactively collect the preference-related information without any modification of clients and video providers. (2) Second, we propose a tree-structure model to learn and compress users’ preferences. (3) Third, to decide which videos should be admitted to the cache in real-time, an explore-and-exploit method is applied. We carried out extensive experiments with 24 hours of trace data from a large commercial video content provider. The experimental results demonstrate that PrefCache can improve hit ratio up to 12%, and save 92% memory / 98% CPU overhead, compared to the state-of-the-art cache policies.

A Study on Video Content Application Based on Mobile Device Platform in China

In this paper, the write analysis the application of basic video content of Mobil Device Platform in China and studies the future development scheme. According to the survey, as of 2019, 78% of Chinese total population is the Internet service users and application software users. Mobile Device Platform is independently developed to provide video content application services, which already have 640 million video application users from 2013 to the present. 65% of users install and use more than two video content applications. In China TikTok, Kuai Shou, MeiPai and other video content applications, not only can easily achieve simple user interface and users can directly shoot video content. These production functions is different from YouTube, a famous video platform in the United States. In the video platform market, the core competitiveness is content creation. In the future, the integration of video content of VR, AR and other video projects will expecte to further activate the video platform market.