17,963 research outputs found

    An autonomic delivery framework for HTTP adaptive streaming in multicast-enabled multimedia access networks

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    The consumption of multimedia services over HTTP-based delivery mechanisms has recently gained popularity due to their increased flexibility and reliability. Traditional broadcast TV channels are now offered over the Internet, in order to support Live TV for a broad range of consumer devices. Moreover, service providers can greatly benefit from offering external live content (e. g., YouTube, Hulu) in a managed way. Recently, HTTP Adaptive Streaming (HAS) techniques have been proposed in which video clients dynamically adapt their requested video quality level based on the current network and device state. Unlike linear TV, traditional HTTP- and HAS-based video streaming services depend on unicast sessions, leading to a network traffic load proportional to the number of multimedia consumers. In this paper we propose a novel HAS-based video delivery architecture, which features intelligent multicasting and caching in order to decrease the required bandwidth considerably in a Live TV scenario. Furthermore we discuss the autonomic selection of multicasted content to support Video on Demand (VoD) sessions. Experiments were conducted on a large scale and realistic emulation environment and compared with a traditional HAS-based media delivery setup using only unicast connections

    Overlay networks for smart grids

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    Crowdsourced Live Streaming over the Cloud

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    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

    On the merits of SVC-based HTTP adaptive streaming

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    HTTP Adaptive Streaming (HAS) is quickly becoming the dominant type of video streaming in Over-The-Top multimedia services. HAS content is temporally segmented and each segment is offered in different video qualities to the client. It enables a video client to dynamically adapt the consumed video quality to match with the capabilities of the network and/or the client's device. As such, the use of HAS allows a service provider to offer video streaming over heterogeneous networks and to heterogeneous devices. Traditionally, the H. 264/AVC video codec is used for encoding the HAS content: for each offered video quality, a separate AVC video file is encoded. Obviously, this leads to a considerable storage redundancy at the video server as each video is available in a multitude of qualities. The recent Scalable Video Codec (SVC) extension of H. 264/AVC allows encoding a video into different quality layers: by dowloading one or more additional layers, the video quality can be improved. While this leads to an immediate reduction of required storage at the video server, the impact of using SVC-based HAS on the network and perceived quality by the user are less obvious. In this article, we characterize the performance of AVC- and SVC-based HAS in terms of perceived video quality, network load and client characteristics, with the goal of identifying advantages and disadvantages of both options
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