Live Streaming Services Research Articles

Relay Streaming System Model to Social Media Platform for Indoor Activity Broadcasting

The purpose of this research is to design a relay streaming model that can be streamed to several social media that provide live streaming services. The research method includes the analysis process which is divided into 4 stages. The first stage is an analysis of live streaming services on a social media. The second step is analysing the need for the server to be able to relay streams. The third stage is an analysis of the integration of the relay server system with live streaming social media services. The last stage is to design a system model based on previous analysis and determine the software requirements specification. The final result of this research is a relay streaming model with a server using Rasberry Pi which is integrated into Facebook and YouTube social media live streaming services. The development of this model is expected to solve the problem of limited indoor wireless networks and video input sources from various recording devices in relay streaming to several live streaming social media services simultaneously

RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videos

With the advancement of rich media generating devices, the proliferation of live Content Providers (CP), and the availability of convenient internet access, crowdsourced live streaming services have witnessed unexpected growth. To ensure a better Quality of Experience (QoE), higher availability, and lower costs, large live streaming CPs are migrating their services to geo-distributed cloud infrastructure. However, because of the dynamics of live broadcasting and the wide geo-distribution of viewers and broadcasters, it is still challenging to satisfy all requests with reasonable resources. To overcome this challenge, we introduce in this paper a prediction driven approach that estimates the potential number of viewers near different cloud sites at the instant of broadcasting. This online and instant prediction of distributed popularity distinguishes our work from previous efforts that provision constant resources or alter their allocation as the popularity of the content changes. Based on the derived predictions, we formulate an Integer-Linear Program (ILP) to proactively and dynamically choose the right data center to allocate exact resources and serve potential viewers, while minimizing the perceived delays. As the optimization is not adequate for online serving, we propose a real-time approach based on Reinforcement Learning (RL), namely RL-OPRA, which adaptively learns to optimize the allocation and serving decisions by interacting with the network environment. Extensive simulation and comparison with the ILP have shown that our RL-based approach is able to present optimal results compared to heuristic-based approaches.

Soft Actor–Critic DRL for Live Transcoding and Streaming in Vehicular Fog-Computing-Enabled IoV

With the rapid development of automotive industry and telecommunication technologies, live streaming services in the Internet of Vehicles (IoV) play an even more crucial role in vehicular infotainment systems. However, it is a big challenge to provide a high quality, low latency, and low bitrate variance live streaming service for vehicles due to the dynamic properties of wireless resources and channels of IoV. To solve this challenge, we propose a novel live video transcoding and streaming scheme that maximizes the video bitrate and decreases time-delays and bitrate variations in vehicular fog-computing (VFC)-enabled IoV, by jointly optimizing vehicle scheduling, bitrate selection, and computational/spectrum resource allocation. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels of IoV. A soft actor–critic deep reinforcement learning (DRL) algorithm that is based on the maximum entropy framework, is subsequently utilized to solve the above MDP. Extensive simulation results based on the data set of the real world show that compared to other baseline algorithms, the proposed scheme can effectively improve video quality while decreasing latency and bitrate variations, and access excellent performance in terms of learning speed and stability.

Optimizing Social Welfare of Live Video Streaming Services in Mobile Edge Computing

The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.

China’s New Mania for Live Streaming: Gender Differences in Motives and Uses of Social Live Streaming Services

ABSTRACT This study examined gender differences in social live streaming service (SLSS) use by focusing on audience needs for Real-life Communication, Escape, Fun Seeking, Partnership Seeking, and Social Interaction. Results of an online survey (N = 312) of Chinese SLSS audiences’ reasons for SLSS use, preferred genres, amount of usage, actual media behavior are reported. Based on Uses and Gratifications theory, we didn’t find enough evidence to show that the man-dominated image of SLSS use cultivated by Chinese media is true, and we argued that the exaggerated gender differences regarding audiences’ use levels and rewarding expenses were without merit. Nevertheless, we did find some differences due to gender. For example, male audiences are more likely to watch and prefer live game broadcasting, and their mediated communication is more active than females.

Sustainability of Live Video Streamer’s Strategies: Live Streaming Video Platform and Audience’s Social Capital in South Korea

Live streamers’ power and attraction influence consumer behavior. This study focuses on streamer-central formed social capital and the relationship between streamers and audiences on live streaming video platforms (LSVP). First, we explored the impact of trust, norm of reciprocity, and network on social capital formation. Second, we investigated the effect of social capital on streamers’ attributes (attractiveness, expertise, and trustworthiness) and on the audience’s social capital formation. The main findings show that trust and network positively affect social capital. Social capital increases the level of streamers’ attractiveness, expertise, and trustworthiness perceived by the audience, which facilitates sustainable development of the LSVP and the streamer. Perceived streamers’ attractiveness negatively affects social capital formation, while perceived expertise positively affects it. To promote social capital development, streamers and operators of LSVPs should continuously emphasize social capital formation. Moreover, LSVPs should provide audiences with novel and interesting content to enable active networking. For sustainable development of LSVPs, when providing live streaming video services, streamers should deliver content that the audience perceives as based on their expertise rather than on their physical attractiveness.

Factors Affecting the Popularity of Video Content on Live-Streaming Services: Focusing on V Live, the South Korean Live-Streaming Service

Naver V Live, a South Korean live-streaming service, showcases video contents specific to the entertainment industry, such as K-pop and music. On V Live, K-pop stars and their fans can interact directly in a natural way, and V Live provides high-quality video content with novel topics. This study has identified key characteristics of video content that affect its popularity. A total of 620 video contents of five leading Star channels were classified on the basis of production company, type of video content, and whether it was live-streamed or not. The popularity of video content was measured by the number of comments, hearts, and views. To control potential bias, additional variables were set as control variables—such as the number of channel subscribers, mini-album sales, if the video content was previewed, and cumulative number of days since the video content was uploaded. For analysis, a hierarchical linear regression was conducted. The findings suggest future directions in video content planning.

Viewing sports online together? Psychological consequences on social live streaming service usage

Social live streaming services (SLSSs) have recently been introduced as a new type of synchronous social networking services for sport fans. Given the popularity of these innovations, the authors examined how the use of SLSSs can augment users’ social well-being and mitigate loneliness through the mechanisms of team identification, flow experiences, and satisfaction with SLSSs. To this end, the researchers collected data from 231 SLSS users who watched sport events over the SLSS platforms during the past 12 months. Results indicated that four types of gratification expectations positively influenced the users’ flow states. Among highly identified users with their preferred teams, affective gratification exerted the greater impact on the flow, predicting satisfaction with SLSSs. Such satisfaction enabled the users to enhance their social well-being and ease their feelings of loneliness. This study provides empirical evidence and new insights that elevate our understanding of SLSSs for sports content.

Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming

With the rise of time-critical and interactive scenarios, ultra-low latency has become the most urgent requirement. Adaptive bitrate (ABR) schemes have been widely used in reducing latency for live streaming services. However, the traditional solutions suffer from a key limitation: they only utilize coarse-grained chunk to solve the I-frame misalignment problem in different bitrate switching process at the cost of increasing latency. As a result, existing schemes are difficult to guarantee the timeliness and granularity of control in essence. In this paper, we use a frame-based approach to solve the I-frame misalignment problem and propose a video adaptation bitrate system (Vabis) in units of the frame for time-critical live streaming to obtain the optimal quality of experience (QoE). On the server-side, a Few-Wait ABR algorithm based on Reinforcement Learning (RL) is designed to adaptively select the bitrate of future frames by state information that can be observed, which can subtly solve the problem of I-frame misalignment. A rule-based ABR algorithm is designed to optimize the Vabis system for the weak network. On the client-side, three delay control mechanisms are designed to achieve frame-based fine-grained control. We construct a trace-driven simulator and the real live platform to evaluate the comprehensive live streaming performance. The results show that Vabis is significantly better than the existing methods with decreases in an average delay of 32%-77% and improvements in average QoE of 28-67%.

How are Live-Streaming Services and Social Media Platforms Changing On-Job MBA Students’ Learning? A Case Study for Applying e-Case Live in Management Case-Based Learning in Taiwan

This study examines how social media and live-streaming services can be used to assist on-job MBA students with their learning of Management cases. A learning framework called e-Case Live, which integrates a popular live-steaming broadcast service with social media, is proposed. A total of 48 on-job MBA students participated in this study to learn about the practices of Management in a case-based course. In terms of the two case-learning scenarios, the traditional case-based learning method group (TG) consisted of 23 participants, while the e-Case Live learning method group (e-CaseG) consisted of 25 participants. Students' perceptions were explored in terms of synchronous discussion, asynchronous discussion, and social presence. The results of this study indicate that: (1) compared to traditional classroom instructional methods, most on-job MBA students were more satisfied with e-Case Live in terms of synchronous and asynchronous discussion, but no significant difference was found in terms of social presence; (2) integrating live-streaming services with social media platforms can offer a valuable instructional method for on-job students who practice authentic problem solving by applying what they learn; (3) the e-Case Live framework can effectively enhance student involvement and engagement in understanding the contexts embedded in Management cases; and (4) social media has increased the interaction between teachers and students both in and out of the classroom.

Gendered hate speech in YouTube and YouNow comments: Results of two content analyses

Online hate speech in general, and gendered online hate speech in particular, have become an issue of growing concern both in public and academic discourses. However, although YouTube is the most important social media platform today and the popularity of social live streaming services (SLSS) such as Twitch, Periscope and YouNow is constantly growing, research on gendered online hate speech on video platforms is scarce. To bridge this empirical gap, two studies investigated gendered online hate speech in video comments on YouTube and YouNow, thereby systematically replicating a study by Wotanis and McMillan (2014). Study 1 investigated YouTube in the form of a content analysis of N = 8,000 publicly available video comments that were addressed towards four pairs of female and male German-speaking YouTubers within the popular genres Comedy, Gaming, HowTo & Style, and Sports [Fitness]. Study 2 examined YouNow, with a quantitative content analysis of N = 6,844 publicly available video comments made during the video streams of 16 female and 14 male popular German-speaking YouNowers. Study 1 successfully replicated the findings of Wotanis and McMillan (2014) that compared to male You-Tubers, female YouTubers received more negative video comments (including sexist, racist, and sexually aggressive hate speech) (H1a). In addition, they received fewer positive video comments regarding personality and video content but more positive video comments regarding physical appearance (H2a). Study 2 partly confirmed the earlier findings: It found that, compared to male YouNowers, the video comments received by female YouNowers were more sexist and sexually aggressive, but not generally more hostile or negative (H1b). They received more positive video comments regarding their physical appearance but did not receive fewer positive video comments regarding their personality or the content of their videos (H2b). With some exceptions, the findings of study 2 were comparable to the findings of study 1 (RQ1). In both studies, most effect sizes were small. Overall, females on the video platforms YouTube and YouNow seem to be disproportionately affected by both hostile and benevolent sexism expressed in viewer comments. The results are in line with the Expectation States Theory and the Ambivalent Sexism Theory. The total number of public hate comments was probably underestimated because inappropriate comments can be deleted by moderators and users. Future research directions and practical implications are discussed. Supplementary material can be retrieved from https://osf.io/da8tw

When Crowd Meets Big Video Data: Cloud-Edge Collaborative Transcoding for Personal Livecast

Deep penetration of personal computing devices and high-speed Internet has enabled everyone to be a broadcaster. In this crowdsourced live streaming service, numerous amateur broadcasters lively stream their video contents to viewers around the world. Consequently, these broadcasters generate a massive amount of video data. The set of video sources and recipients are big as well, so are demand for the storage and computational resources. Transcoding becomes a must to better service these viewers with different network and device configurations. However, the massive amount of video data contributed by countless channels even makes cloud significantly expensive for providing transcoding services to the whole community. In this paper, inspired by the paradigm of Edge Computing, we propose a Cloud-edge collaborative system which combines the idle end-viewers' resources with the cloud to transcode the massive amount of videos at scale. Specifically, we put forward tailored viewer selection algorithms after empirically analyses the viewer behavior data. In the meantime, we propose auction-based payment schemes to motivate these viewers participating in the transcoding. Large-scale trace-driven simulations demonstrate the superiority of our approach in cost reduction and service stability. We further implement a prototype in PlanetLab to prove the feasibility of our design.

How live streaming church services promotes social participation in rural areas

How live streaming church services promotes social participation in rural areas

Don't just watch, join in: Exploring information behavior and copresence on Twitch

This mixed-methods study examined users' information behavior and their perceptions of copresence on Twitch.tv, where millions of people come together live every day to stream, interact, and make their own entertainment. Human information theory model and social identity theory constituted the theoretical framework for this research. Studying topic specific live streaming services is an emerging and exciting field in communication. Topic specific live streaming sites such as Twitch.tv are evolving constantly into important sources of information that complement the traditional information systems such as libraries and online search engine sites like Google. Chat logs of three live streams on Twitch.tv were analyzed using Linguistic Query and Word Count (LIWC) and SPSS statistical tools. Qualitative thematic analysis was carried out using Nvivo 12. Information reaction and production were the most frequent information behaviors across three streams. Qualitative analysis indicated that users within the three live streams showed a great deal of copresence. This study is an important first step to provide theoretical insights into understanding human information behavior on Twitch, topic specific live streaming sites, and social live streaming sites in general.

Audiences’ Motives for Watching Live Video Streaming

Due to the popularity and low cost of rapid mobile internet connection, live-streaming service is now booming. Some of these users who provide live video shows to the public are famous and known as vloggers or YouTube celebrities. The content of the live show is diverse. Some YouTubers broadcast video of their computer screen when playing an online game; others cook, work out, or put on makeup on camera. Nearly anything from one’s daily life is fair game. The abundant live video streaming content attracts youngsters as well as adults. To understand the viewers’ motivation for watching live video streaming, this study used a self-reporting online questionnaire survey. A total of 374 responses were received and analyzed to ascertain the motives of viewing live video streaming. Based on the empirical survey results, we propose a motivation of YouTuber model composed of four motive categories: leisure, celebrity worship, social connection, and voyeurism. These four categories can be divided into eight motives, including passing time, entertainment, relaxation, celebrity identification, vicarious participation, companionship, social interaction, and voyeurism.

Vicariously Experiencing it all Without Going Outside

The livestreaming industry in China is gaining greater traction than its European and North American counterparts and has a profound impact on the stakeholders' online and offline lives. An emerging genre of livestreaming that has become increasingly popular in China is outdoor livestreaming. With outdoor livestreams, streamers broadcast outdoor activities, travel, or socialize with passersby in outdoor settings, often for 6 or more hours, and viewers watch such streams for hours each day. However, given that professionally produced content about travel and outdoor activities are not very popular, it is currently unknown what makes this category of livestreams so engaging and how these techniques can be applied to other content or genres. Thus, we conducted a mixed methods study consisting of a survey (N=287) and interviews (N = 20) to understand how viewers watch and engage with outdoor livestreams in China. The data revealed that outdoor livestreams encompass many categories of content, environments and passersby behaviors create challenges and uncertainty for viewers and streamers, and viewers watch livestreams for surprising lengths of time (e.g., sometimes more than 5 continuous hours). We also gained insights into how live commenting and virtual gifting encourage engagement. Lastly, we detail how the behaviors of dedicated fans and casual viewers differ and provide implications for the design of livestreaming services that support outdoor activities.

Addressing Application Latency Requirements through Edge Scheduling

Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.

QoS Aware Transcoding for Live Streaming in Edge-Clouds Aided HetNets: An Enhanced Actor-Critic Approach

With the advances in hand-held devices (smartphones and tablets, etc.) and high speed wireless networks, users have an explosive growth demand for live streaming service. Due to the diversity of user equipments (UEs), the live streaming has to be transcoded into different versions. However, transcoding is a computationally expensive and time consuming process. Due to the dynamic characteristics of wireless network environment, providing high quality and low latency live videos for UEs is a big challenge. This study investigates user scheduling, transcoding decision, computational and wireless spectrum resources allocation problem in edge-clouds aided heterogeneous networks (HetNets). The research focuses on improving UEs’ quality of service (QoS) for live-streaming services, which includes quality of video and latency requirements. Different from existing literature, to approach the real wireless environment, the available computational and wireless spectrum resources are modeled as random processes in the work. Considering dynamic characteristics of wireless networks and available resources, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, it is difficult to solve this problem by traditional learning algorithms. Therefore, an enhanced actor-critic algorithm is proposed to resolve the problem, in which both the actor part and the critic part employ eligibility traces. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm.

Client-Side QoE Management for SVC Video Streaming: An FSM Supported Design Approach

HTTP adaptive streaming (HAS) which provides the flexibility of video bit-rate adjustments, is becoming the de facto framework for live and on-demand video streaming services. The flexibility of HAS is further empowered by the scalable video coding (SVC) technique which allows low overhead quality upgradations of transmitted video segments. In this paper, we present a client-side finite-state machine (FSM) supported quality of experience (QoE) centric video bit-rate adaptation and management mechanism. The proposed strategy simultaneously manages three important QoE verticals: 1) providing stutter-free video viewing experience; 2) minimizing flickers in video outputs by controlling and smoothing the rates of encoding quality switches over time; and 3) maximizing aggregate video quality over a video playout session. Based on careful consideration of these QoE verticals, the management policy dynamically decides whether to download a new segment or upgrade the quality of an already downloaded segment in the playout buffer. We have implemented and evaluated the performance of our proposed framework in a multi-client SVC video steaming test-bed. Conducted experiments using real-world network traces reveal that the proposed strategy is able to outperform other state-of-the-art adaptive streaming techniques and deliver satisfactory QoE even in the face of highly varying channel conditions.

Over-the-top sport: live streaming services, changing coverage rights markets and the growth of media sport portals

The growth of over-the-top (OTT) Internet and mobile video streaming services is a major development in the distribution, transmission and consumption of global media sport. Heavily capitalised services such as Tencent Video, DAZN and Amazon Prime Video are intervening in coverage rights markets and changing how live sport is experienced and shared across television, computer, game console, tablet and smartphone screens. This article identifies and analyses six defining characteristics of OTT live sport streaming, and outlines three services (Tencent Video, DAZN and Amazon Prime Video) that operate across Asia, the United Kingdom, Europe, the Americas and Australasia. Its argument is that, first, live sport streaming is a key means by which television content and practices are escaping the boundaries of broadcast media, while also continuing to perpetuate the logics of television coverage and viewing practices. Second, drawing on Amanda D. Lotz’s conceptualisation of portals, it is proposed that these services are establishing new norms concerning how media sport is accessed and curated and, as such, their arrival signals a historic shift in the global marketplace for sport coverage rights and the media systems through which live content circulates.