Online Video Streaming Services Research Articles

On-demand online video streaming services: a bibliometric analysis and future research agenda

ABSTRACT An exponential increase in online video streaming literature on OTT platforms during and post-COVID indicates the growing popularity of streaming services. The present bibliometric review analyses 324 publications from Web-of-Science indexed journals to explore current trends in the on-demand online video streaming service’s literature. The review also displays the thematic map of keywords to identify gaps. The trend analysis confirmed an exponential growth in publications with a 91.58% explanation. The findings suggest (1) common terminologies for online streaming video terms, (2) more inter and intra-country author collaborations, and (3) An IPO (Inputs – Process – Outcomes) framework.

Factor structure, reliability and criterion-related validity of the English version of the Problematic Series Watching Scale.

Psychological research in the past decade has investigated the psychosocial implications of problematic use of on-demand online video streaming services, particularly series watching. Yet, a psychometric measure of problematic series watching in English is not available. The present study aimed to test the factor structure, reliability and criterion-related validity of the English version of the Problematic Series Watching Scale, a six-item self-report assessing problematic series watching, based on the biopsychosocial components model of addiction. Participants were recruited from two UK university student samples. Study 1 (n = 333) comprised confirmatory factor analysis, reliability tests and item response theory analyses to test the original unidimensional model and investigate each item's levels of discrimination and information. Study 2 (n = 209) comprised correlation analyses to test the criterion-related validity of the scale. There was a good fit of the theoretical model of the scale to the data (Comparative Fit Index = 0.998, Root Mean Square Error of Approximation = 0.024 [90% CI 0.000-0.093], Standardised Root Mean square Residual = 0.048), satisfactory reliability (ω = 0.79) and item levels of discrimination and information. The scale positively correlated with time spent watching series (rs = 0.26, P < 0.001) and negative affect (rs = 0.43, P < 0.001), and correlated negatively with positive affect (rs = -0.12, P > 0.05), mental well-being (rs = -0.25, P < 0.001) and sleep quality (rs = -0.14, P < 0.05). Results are discussed in relation to the ongoing debate on binge watching and series watching in the context of positive reinforcement versus problematic behaviour.

DASH Streaming traffic influence over energy efficient ethernet to improve energy savings

Dynamic Streaming over HTTP (DASH) is the main standard used in online video streaming services, given that more than 1.2 billion pay subscribers around the world use this standard. This fact entails billions of streaming connections between video servers and client displays. These devices involved in the streaming connection use an Ethernet Interface Card that consumes energy. In order to reduce the energy consumption, IEEE proposed the 802.3az Energy Efficient Ethernet standard, with a mechanism to make the network card change to a low power consumption mode when it is not in transmission mode. This behavior will be beneficial for services where traffic is sent in bursts, for instance video packet bursts like in video streaming over Real Time Transport Protocol in IPTV or the widely used DASH standard. Therefore, in this study the Ethernet traffic pattern when transmitting online video content using DASH is characterized in order to analyze the efficiency of the IEEE 802.3az standard under this video streaming scenario, and to verify the convenience of activating this energy saving alternative at the network interface of billions of client devices. The experiments have been conducted using a test-bed consisting of a full DASH streaming architecture, comparing different video segment sizes and changing the available bandwidth during the experiments in different scenarios in order to analyze the effect of the DASH content segment size on the Ethernet traffic pattern to identify the trade-off between energy efficiency, the energy savings, and the impact on the performance of the dynamic adaptation on the video streaming and reproduction.

On Privacy Risks of Watching YouTube over Cellular Networks with Carrier Aggregation

One's core values, personality, and social status may be reflected in the watch history of online video streaming services such as YouTube. Unfortunately, several prior research work demonstrates that man-in-the-middle or malware-assisted attackers can accurately infer the titles of encrypted streaming videos by exploiting the inherent correlation between the encoded video contents and the traffic rate changes. In this paper, we present a novel video-inference attack called Moba that further exacerbates the problem by only requiring the adversary to simply eavesdrop the broadcast messages of a primary cell of a targeted user's cellular phone. Our attack utilizes a side channel in modern cellular networks that leaks the number of actively transmitting cells for each user. We show that this seemingly harmless system information leakage can be used to achieve practical video-inference attacks. To design effective video-inference attacks, we augment the coarse-grained side-channel measurements with precise timing information and estimate the traffic bursts of encrypted video contents. The Moba attack considers an adversary-chosen set of suspect YouTube videos, from which a targeted user may watch some videos during the attack. We confirm the feasibility of Moba in identifying the exact YouTube video title (if it is from the suspect set) via our over-the-air experiments conducted in LTE-Advanced networks in two countries. Moba can be effective in verifying whether a targeted user watches any of the suspect videos or not; e.g., precision of 0.98 is achieved after observing six-minutes of a single video play. When further allowed to observe multiple video plays, Moba adversary is able to identify whether the targeted user frequently watches the suspect videos with a probability close to one and a near-zero false positive rate. Finally, we present a simple padding-based countermeasure that significantly reduces the attack effectiveness without sacrificing any cellular radio resources.

Customer Benefits and Sharing Behaviour in Video Streaming Services: The Mediation Effect of Value Creation

Online video streaming services have become popular. This study aims to examine subscribers’ sharing behaviour on online digital video platforms. In almost all online channels, sharing behaviour has gained importance. Platforms may more simply and successfully reach their target audiences or consumers through sharing. On the other hand, customers may attend to value creation due to the benefits they gain from using goods and services. Marketers must comprehend how value creation is affected by customer benefits. This study is among the first studies examining the relationships between customer benefits, value creation, and sharing behaviour. Questionnaire-based research was employed to examine the foundation and framework-related hypotheses. Data was collected through the online survey forms. The data was obtained from 363 online streaming service paid users. The Structural Equation Model was performed using SPSS Amos 20 to test hypotheses. The statistical significance of the model’s estimations was tested using a single-step mediator model with a bootstrapping procedure. According to the findings, there was a significant relationship between customer benefits and instrumental value. However, there were very weak relationships between functional and psychosocial benefits and instrumental value. Financial benefit and instrumental value had a stronger relationship. Significant relationships were found between the variables customer benefits and terminal value. There was no significant relationship between instrumental value and sharing behaviour. As a result, there was no instrumental value-mediated relationship between customer benefits and sharing behaviour. The relationship between customer benefits and sharing behaviour, on the other hand, has been found to be mediated by terminal values. Functional and financial benefits and sharing behaviour did not directly and significantly relate. But this relationship became significant through terminal values (indirect-only mediation). Psychosocial benefits and sharing behaviour were directly and significantly related. It has been noted, nonetheless, that this relationship was strengthened by terminal value. The findings were assessed in light of the prior studies in the discussion section. In this section, some potential directions were suggested for future research.

Life Cycle Assessment of CO2 Emissions of Online Music and Videos Streaming in Japan

In this study, we analyzed the CO2 emissions of online music and video streaming services, as one of the digital contents, in Japan using life cycle assessment. As a system boundary of online music and video streaming, processes such as data center construction and server manufacturing, usage of communication networks and internet communication technology devices (personal computers (PCs) and smartphones), and disposal of data centers and servers were considered. Data were collected using statistical and online surveys, and CO2 emissions per 1 MB of communication volume were calculated. One of the results revealed that the lifecycle CO2 emissions of listening to online music using PCs and smartphones were 5.88 × 10−4 and 1.43 × 10−4 kg-CO2/MB, respectively. The overall CO2 emissions for domestic music and video streaming services in 2019 was 921 thousand t-CO2. Online video streaming accounted for 87.7% of the total emissions, which corresponded to approximately 0.23% of domestic CO2 emissions derived from electric power generation.

Reciprocating Business Model Innovation – How Client and Supplier Models Interact

ABSTRACT Purpose. The purpose of this article is to show the relationship between changes of industry business model on the supplier side and changes in business model on the client side, using the information technology industry as an example. This is the first paper to investigate supplier-side industry-wide business models and identify their consequences for client-side industry-wide business models. Methodology. The methodology is a review of academic and gray literature and conceptual analysis, applied particularly to three client-side industry case studies – financial services, airlines and online video streaming services Findings. Changes in the business model on the client side may be contingent on the products and services that emerge from the information and communications technology industry as it changes its business models. Practical implications. Client-side companies formulating their business strategies in industries which are highly dependent on successful information management should factor developments in the information and communications technology industry business models into account in their planning. They should also consider how they can influence business model change on the supplier side by changes they make to their information management strategies and processes. Research implications. The implications for research are that researching investigating changes in business models, particularly in relation to clients of the IT industry, should consider whether there have been changes in business models on the supplier or client side, and whether and how they have interacted.

Public service media in the age of SVoDs: A comparative study of PSM strategic responses in Flanders, Italy and the UK

Netflix and other transnational online video streaming services are disrupting long-established arrangements in national television systems around the world. In this paper we analyse how public service media (PSM) organisations (key purveyors of societal goals in broadcasting) are responding to the fast-growing popularity of these new services. Drawing on Philip Napoli’s framework for analysing strategic responses by established media to threats of competitive displacement by new media, we find that the three PSM organisations in our study exhibit commonalities. Their responses have tended to follow a particular evolution starting with different levels of complacency and resistance before settling into more coherent strategies revolving around efforts to differentiate PSM offerings, while also diversifying into activities, primarily across new platforms, that mimic SVoD approaches and probe production collaborations. Beyond these similarities, however, we also find that a range of contextual factors (including path-dependency, the role and status of PSM in each country, the degree of additional government support, cultural factors and market size) help explain nuances in strategic responses between our three cases.

Dual band radio-over-fibre millimetre–wave system utilizing optical frequency combs

Abstract The urge for high-speed data transmission never ends. The demand for seamless online video streaming services demands a faster data transmission rate. To fulfil the demand optical communication can be used to enhance the overall performance. The union of microwave and optical networks known as Radio-over-Fibre (RoF) technology, may be a reasonable solution for improving capacity and mobility likewise as decreasing costs within the access network. Millimetre-wave (mm-wave) signals can be utilized for multi-Gb/s transmission. But mm-wave generation in the electrical domain is less feasible. Here, the optical domain comes into play. In this paper, two mm-wave frequencies are generated using optical frequency combs (OFCs). This technique is an effective solution for the mm-wave RoF system for 5G and beyond. By simulation, the major restraints of linewidth and relative intensity noise (RIN) are explored. RIN level of −163 dB/Hz is achieved.

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.

Customer Perception towards Networked Streaming Service Providers with Reference to Amazon Prime and Netflix

Online video streaming services like Amazon Prime and Netflix have redefined the concept of movie watching and entertainment. They have become hugely popular worldwide. Their subscribers are increasing day by day. There is also intense competition between the two for getting viewer attention. In this context, the study aims at understanding the viewer perception about the two online service providers through viewer opinions collected from different social media platforms like Instagram, Facebook, Twitter, blogs, discussion forums, etc. Viewer opinions expressed online becomes electronic word-of-mouth for potential viewers who often assess the two platforms using the reviews of their peers. The opinions were checked for sentiments relating to content, audio/video quality, ease of use, and price related aspects of Netflix and Amazon Prime using the lens of e-service quality. The study analyses how sentiment analysis in Python has brought out the most favored service provider in terms of positive viewer reviews. For the study, the primary data was collected from reviews and was analyzed to look for patterns using the online text mining software called Voyant tool. The result of study states that Ease of use is an important aspect of e-service quality since both Amazon and Netflix are offered through technology aided platforms

Study of Perception of College Going Young Adults towards Online Streaming Services

The research conducted focuses upon the perception of college going young adults towards online video streaming services. Researcher has worked upon responses gathered from young adults, their perceptions and various options available to them. Researchers collected responses from 120 college going young adults from Pune. The respondents were from the age category of 15 to 25 years of age. The data was collected using google forms and it was analysed using Google’s analytical tools. It was found that most of the student services and these services proved to be one of the biggest sources of entertainment for these students. Traditional media is losing its lustre because of various advantages of streaming services. Students admitted that their schedule is affected because of time spent on video streaming services. Today the top three video streaming platforms are YouTube, Netflix and Hotstar.

Intelligent sentiment analysis approach using edge computing‐based deep learning technique

SummarySentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.

Finding the Sweet Spot: Ad Scheduling on Streaming Media

A majority of US households view content on online video streaming services, consuming on demand. Not surprisingly, ad spending on such services is growing rapidly. We develop a three-stage approach to deliver an optimal ad schedule that balances the interests of the viewer (content consumption) with that of the streaming platform (ad exposure). In the first stage, we use theoretical findings to develop two parsimonious metrics – Bingeability and Ad Tolerance – to capture the interplay between content consumption and ad exposure. Bingeability represents the number of completely viewed unique episodes of a show while Ad Tolerance represents the willingness of a viewer to continue watching after ad exposure. The second stage uses detailed data on viewing activity and ad delivery to predict these metrics for a viewing session using causal machine learning methods. This is achieved via tree-based algorithms combined with instrumental variables to accommodate the non-randomness in ad delivery. In the third stage, we use the predicted metrics as inputs to a novel constrained optimization procedure that provides the optimal ad schedule.

A Review of Predictive Quality of Experience Management in Video Streaming Services

Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (QoE) expectations. QoE management is an end-to-end process that, due to the ever-increasing variety of video services, has become too complex for conventional “reactive” techniques. Herein, we review the most significant “predictive” QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services.

Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives

With the growing relevance of the Internet as a tool for communication and entertainment, researchers have examined the effects of individual's psychological factors and media use motives on their excessive use of Internet. Since Internet use has significant nuances, the excessive use of particular Internet application, such as social network services and online games, has been studied separately. However, as the major Internet application, online video streaming service has not been investigated. Moreover, other than psychological factors and media use motives, the IT features implemented in the application, such as the recommender system, could also induce excessive use behavior. This paper aims to examine individual's excessive use of online video streaming services and the effects of recommender system in such services. A survey of 490 video streaming service users was conducted. The results show that the use of recommendations, along with lack of self-control, lack of self-esteem and use motive of information seeking, lead to excessive usage of video streaming services. This study contributes to the literature of excessive Internet use by exploring individual's excessive use behavior in video streaming services and incorporating the salient role of recommender system in Internet applications.

Customer benefits and value creation in streaming services marketing: a managerial cognitive capability approach

AbstractCustomer value analysis and management is a key theoretical and empirical issue in marketing management and strategic management. However, little is known about the influence of customer benefit on customer value from the microfoundations of the dynamic capabilities perspective. Currently, a boom in online video and music streaming services is changing the entertainment industry structure. Thus, marketing managers in the fast‐growing streaming services industry should have dynamic managerial capabilities to anticipate other service elements that customers consider valuable. Based on managerial cognitive dynamic capabilities, this research explored the influences of customer‐perceived functional benefit, experiential benefit, financial benefit, and psychosocial benefit on perceived instrumental and terminal values. General linear model (GLM) and fuzzy‐set qualitative comparative analysis (fsQCA) were conducted to gain a more nuanced understanding of how different customers’ perceived benefits have different impacts on perceived value. The findings illuminate complex benefit configurations that drive perceived instrumental and terminal values and contribute to the development of value creation and its drivers. The proposed framework can help managers develop managerial cognitive dynamic capabilities by increasing their understanding of the impact of different perceived benefits on value creation for different types of customers.

Television and The Digital Revolution: A Chance for Survival

It is argued that today’s world is marked by competition that negatively impacts the television industry which has begun losing viewers. Despite this loss of market share, television industry can survive the digital revolution by leading the way with innovation, adapting to the happenings in the niche, meeting the needs of viewers with so many options, and adopting a business model that is different, distinctive, and competitive. The discussion that employs persuasion as a method demonstrates that television is much stronger than becoming obsolete, and that the survival of television is determined not only by the change of content but also its delivery. This survival is also determined by audiences who are not for switching over to online video streaming services, and who prefer television to other outlets because of its programs, especially sports and shows. It has been demonstrated that television has its own place, that it is capable of competing with online services, and that it will be triumphant. DOI: 10.5901/mjss.2016.v7n4p572

Measurement of Quality of Experience of Video-on-Demand Services: A Survey

Video-on-demand streaming services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017, Cisco, 2014. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step toward providing a satisfactory service is to be able to quantify the users' perception of the current service level. Quality of experience (QoE) is a quality metric that provides a holistic measure of the users' perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services.

A QoS-Enabled Transmission Scheme for MPEG Video Streaming

While MPEG is the de facto encoding standard for video services, online video streaming service is becoming popular over the open network such as the Internet. As the performance of open network is non-predictable and uncontrollable, the tuning of the quality of service (QoS) for on-line video streaming services is difficult. In order to provide better QoS for the delivery of videos, there are proposals of new encoding formats or new transmission protocols for on-line video streaming. However, these results are not compatible with popular video players or network protocols and hence these approaches are so far not very successful. We use another approach which tries to by-pass these problems. We designed a QoS Tuning Scheme and a QoS-Enabled Transmission Scheme for transmitting MPEG videos from video servers to clients. According to the traffic characteristics between the video server and each individual client, the QoS Tuning Scheme tunes the QoS to be delivered to each individual client on the fly. Furthermore, our QoS-Enabled Transmission Scheme can be applied over any protocol, such as HTTP which is the most popular protocol over the open network. With our transmission scheme, bandwidth can be better utilized by reducing transmitted frames which would have missed their deadlines and would eventually be discarded by the clients. This is achieved by sending frames according to their impact on the QoS in the playback under the allowed throughput. With these schemes, users can enjoy video streaming through their favorite video players and with the best possible QoS. In order to facilitate the real time QoS tuning, a metric, QoS-GFS, is developed. This QoS-GFS is extended from the QoS-Index, another metric which has taken human perspective in the measurement of video quality. Hence QoS-GFS is better than the common metrics which measures QoS by means of rate of transmission of bytes or MPEG frames. We designed and implemented a middleware to perform empirical tests of the proposed transmission scheme and QoS tuning scheme. Experiment results show that our schemes can effectively enhance the QoS for online MPEG video streaming services.