3,368 research outputs found
Smart PIN: utility-based replication and delivery of multimedia content to mobile users in wireless networks
Next generation wireless networks rely on heterogeneous connectivity technologies to support various rich media services such as personal information storage, file sharing and multimedia streaming. Due to usersâ mobility and dynamic characteristics of wireless networks, data availability in collaborating devices is a critical issue. In this context Smart PIN was proposed as a personal information network which focuses on performance of delivery and cost efficiency. Smart PIN uses a novel data replication scheme based on individual and overall system utility to best balance the requirements for static data and multimedia content delivery with variable device availability due to user mobility. Simulations show improved results in comparison with other general purpose data replication schemes in terms of data availability
vSkyConf: Cloud-assisted Multi-party Mobile Video Conferencing
As an important application in the busy world today, mobile video
conferencing facilitates virtual face-to-face communication with friends,
families and colleagues, via their mobile devices on the move. However, how to
provision high-quality, multi-party video conferencing experiences over mobile
devices is still an open challenge. The fundamental reason behind is the lack
of computation and communication capacities on the mobile devices, to scale to
large conferencing sessions. In this paper, we present vSkyConf, a
cloud-assisted mobile video conferencing system to fundamentally improve the
quality and scale of multi-party mobile video conferencing. By novelly
employing a surrogate virtual machine in the cloud for each mobile user, we
allow fully scalable communication among the conference participants via their
surrogates, rather than directly. The surrogates exchange conferencing streams
among each other, transcode the streams to the most appropriate bit rates, and
buffer the streams for the most efficient delivery to the mobile recipients. A
fully decentralized, optimal algorithm is designed to decide the best paths of
streams and the most suitable surrogates for video transcoding along the paths,
such that the limited bandwidth is fully utilized to deliver streams of the
highest possible quality to the mobile recipients. We also carefully tailor a
buffering mechanism on each surrogate to cooperate with optimal stream
distribution. We have implemented vSkyConf based on Amazon EC2 and verified the
excellent performance of our design, as compared to the widely adopted unicast
solutions.Comment: 10 page
Final report on the evaluation of RRM/CRRM algorithms
Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin
Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs
Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP's operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanismThis work was supported in part by the EC projects Fed4Fire+, under Grant 732638 (H2020-ICT-13-2016, Research and Innovation Action), and in part by Open-VERSO project (Red Cervera Program, Spanish Government's Centre for the Development of Industrial Technology
AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction
Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clientsâ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clientsâ upstream capac- ities â typically much lower than downstream capacities â which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clientsâ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF
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