Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, crowdsourced streaming generalizes the single-source streaming paradigm by including massive contributors for a video channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the realworld, we have built a prototype system running over the planetlab and the Amazon Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.
Empowered by today's rich tools for media generation and collaborative production, and the convenient Internet access, crowdsourcing further extends the single-source paradigm. It combines the efforts of multiple self-identified contributors, known as crowdsourcers , for a greater result, and has seen success in many areas @cite_3 . For example, LiFS (Locating in Fingerprint Space) was developed for wireless indoor localization with smartphones based crowdsourcing @cite_12 . @cite_13 used crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces shared by other devices in cellular networks. For video applications, a scalable system that allows users to perform content-based searches on continuous collection of crowdsourced video was proposed in @cite_4 . @cite_0 investigated the the crowdsourcing of personal and social traits in online social video or social media content in general. Recently, Youtube has integrated with Google Moderator, a crowdsourcing and feedback production, to increase the engagement between viewers and content creators. Such other video sharing sites as Poptent and VeedMe have also opened interfaces for crowdsourcers with user generated content. Crowdsourced live streaming services have emerged in the market as well, especially for streaming sports online broadcast. Examples include Stream2Watch.me and sportLEMON.tv.
@cite_4 @cite_3 @cite_0 @cite_13 @cite_12
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With the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, end-users are no longer pure content consumers, but contributors, too. In today's crowdsourced streaming systems, numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are geo-distributed globally and highly heterogeneous in terms of the generated video quality and the network system configurations. The scalability and heterogeneity challenges therefore lie on both broadcasters and the viewers, which call for massive transcoding, and two critical issues: 1) choosing video representation set that maximizes viewer satisfaction and 2) allocating computational resources that minimize operational costs, must be systematically optimized in the global scale. In this paper, we present a generic framework utilizing the powerful and elastic cloud computing services for crowdsourced live streaming with heterogeneous broadcasters and viewers. We jointly consider the viewer satisfaction and the service availability pricing of geo-distributed cloud resources for transcoding. We develop an optimal scheduler for allocating cloud instances with no regional constraints. We then extend the solution to accommodate regional constraints, and discuss a series of practical enhancements, including popularity forecasting, initialization latency, and viewer feedbacks. Our solutions have been evaluated under diverse networks and cloud system configurations as well as parameter settings. The trace-driven simulation confirms the superiority of our design, while our Planetlab-based experiment offers further practical hints toward real-world migration. While multimedia and social computing research have used crowdsourcing techniques to annotate objects, actions, and scenes in social video sites like YouTube, little work has addressed the crowdsourcing of personal and social traits in online social video or social media content in general. In this paper, we address the problems of (1) crowdsourcing the annotation of first impressions of video bloggers (vloggers) personal and social traits in conversational YouTube videos, and (2) mining the impressions with the goal of modeling the interplay of different vlogger facets. First, we design a human annotation task to crowdsource impressions of vloggers that extends a tradition of studies of personality impressions with the addition of attractiveness and mood impressions. Second, we propose a probabilistic framework using Topic Models to discover prototypical impressions that are data driven, and that combine multiple facets of vloggers. Finally, we address the task of automatically predicting topic impressions using nonverbal and verbal content extracted from videos and comments. Our study of 442 YouTube vlogs and 2,210 annotations collected in Mechanical Turk supports recent literature showing the feasibility to crowdsource interpersonal human impression with comparable quality to what is reported in social psychology research, and provides insights on the interplay among human first impressions. We also show that topic models are useful to discover meaningful prototypical impressions that can be validated by humans, and that different topics can be predicted using different sources of information from vloggers’ nonverbal and verbal content, as well as comments from the audience. Online labor markets, such as Amazon's Mechanical Turk, have been used to crowdsource simple, short tasks like image labeling and transcription. However, expert knowledge is often lacking in such markets, making it impossible to complete certain classes of tasks. In this work we introduce an alternative mechanism for crowdsourcing tasks that require specialized knowledge or skill: communitysourcing --- the use of physical kiosks to elicit work from specific populations. We investigate the potential of communitysourcing by designing, implementing and evaluating Umati: the communitysourcing vending machine. Umati allows users to earn credits by performing tasks using a touchscreen attached to the machine. Physical rewards (in this case, snacks) are dispensed through traditional vending mechanics. We evaluated whether communitysourcing can accomplish expert work by using Umati to grade Computer Science exams. We placed Umati in a university Computer Science building, targeting students with grading tasks for snacks. Over one week, 328 unique users (302 of whom were students) completed 7771 tasks (7240 by students). 80 of users had never participated in a crowdsourcing market before. We found that Umati was able to grade exams with 2 higher accuracy (at the same price) or at 33 lower cost (at equivalent accuracy) than traditional single-expert grading. Mechanical Turk workers had no success grading the same exams. These results indicate that communitysourcing can successfully elicit high-quality expert work from specific communities. In recent years, empowered by rich media generation devices and convenient Internet access, Crowdsourced Live Game Video Streaming (CLGVS) has become one of the most popular Internet services. Twitch.tv, the most well-known CLGVS platform in the world, allows gamers to broadcast their gaming videos over the Internet. With the prevalence of mobile devices, viewers can watch gamers playing video games anywhere, anytime, on any devices (e.g., smartphones, tablets, or personal computers). However, the heterogeneity of user devices makes conventional solutions hard to ensure user-perceived quality. In this paper, we address the problem of cost-effective adaptive live game video streaming from the perspective of CLGVS service providers. Our purpose is to minimize the operational cost for CLGVS service providers by making live transcoding decisions, bit-rate adaptation decisions, and datacenter assignment decisions dynamically. Meanwhile, our algorithm also ensures good-enough service quality for viewers. Due to the diversity of game genres, we also consider game genres when designing our algorithm. To achieve the above purpose, we formulate the problem into a constrained stochastic optimization problem. By leveraging the Lyapunov optimization framework, we derive the online strategy with provable performance bound. To evaluate the effectiveness of our proposed algorithm, we further conduct a series of trace-driven simulations. The experimental results demonstrate the effectiveness of our algorithm in terms of operational cost and service quality. Our proposed algorithm can reduce operational cost by up to 50 while achieving good-enough viewer QoE compared with other alternatives.