13,465 research outputs found
Device-Centric Cooperation in Mobile Networks
The increasing popularity of applications such as video streaming in today's
mobile devices introduces higher demand for throughput, and puts a strain
especially on cellular links. Cooperation among mobile devices by exploiting
both cellular and local area connections is a promising approach to meet the
increasing demand. In this paper, we consider that a group of cooperative
mobile devices, exploiting both cellular and local area links and within
proximity of each other, are interested in the same video content. Traditional
network control algorithms introduce high overhead and delay in this setup as
the network control and cooperation decisions are made in a source-centric
manner. Instead, we develop a device-centric stochastic cooperation scheme. Our
device-centric scheme; DcC allows mobile devices to make control decisions such
as flow control, scheduling, and cooperation without loss of optimality. Thanks
to being device-centric, DcC reduces; (i) overhead; i.e., the number of control
packets that should be transmitted over cellular links, so cellular links are
used more efficiently, and (ii) the amount of delay that each packet
experiences, which improves quality of service. The simulation results
demonstrate the benefits of DcC
Cooperative announcement-based caching for video-on-demand streaming
Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth
- âŠ