While a number of adaptive m-learning systems have been previously proposed, none of these have thoroughly addressed the adaptation of educational multimedia content. Streaming multimedia content to mobile devices is a resource intensive task that requires significant resources such as network bandwidth, with demand expected to increase beyond future networks capacity as users adopt new technologies such as UHD, AR, VR, 3D and 360-degree video. New challenges include the multitude of mobile devices with different characteristics and limitations, as well as the exponential growth in educational multimedia content. With the proliferation of mobile devices and online video services, mobile learning has grown both in popularity and complexity. Several simulations based on parameters of real mobile devices demonstrate that our method can save an average of almost 21.17% of total energy on different mobile devices and an average of 17.09% of total energy under different transmitting rates than the existing algorithms for 2D-to-3D conversion. The complexity of depth estimation, the processing capability of the cloud, and the power consumption of the mobile are considered jointly into the model to provide an optimized solution. Then, a dynamic offloading model is proposed for mobile energy minimization by allocating the partitions to be processed dynamically between the cloud and the mobile. The cloud-friendly depth estimation algorithm partitions an input image into several parts, classifies each part to a specific type, and applies a specific conversion algorithm to each type to generate depth maps, which facilitates allocating the partitions between the mobile device and the cloud dynamically. In this paper, a dynamic offloading model together with a cloud-friendly depth estimation algorithm is proposed to minimize the energy consumption of mobile devices by exploiting cloud computational resources for 2D-to-3D conversion. The simulation results show that the performance of proposed schemes outperforms the others in cost efficiency while satisfying the delay requirement of the connected users. Then, we present a delay-aware cost-minimizing resource allocation scheme, and follow a tractable approach to derive the performance bounds on blocking probability of the system. Towards this end, we first present an accurate delay model, as the main control parameter for QoE, by considering all sources of delay into account.
Here, we aim at presenting a resource allocation framework for cloud centers which benefits from highly virtualized CPU/GPU resources for cost efficiency. Towards this end, it is required to investigate how one can minimize the cloud costs while satisfying the quality of experience (QoE) requirement of users. Successful deployment of any cloud gaming solution requires an ultra low-delay cost-efficient design. Real-time online gaming via thin clients, which is known as cloud gaming, connects players around the world and let them play high-quality games without need of having much processing capacity. We show power measurements both with a 2D and 3D games, and also additional measurements with a smart TV-stick.
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Our prototype is based on GamingAnywhere open-source software for which we have also integrated a gamepad for easier controls. In this paper, we fill a research gap related to energy efficiency by showing that mobile phone users can save between 12 and 32 % power by utilizing remote gaming instead of playing with a native app.
While the incentives for the game companies are relatively clear, the end-user experience has been investigated mainly from the viewpoint of latency. In such environments, remote cloud gaming can already now be utilized by game companies as an alternative to traditional download-and-install games in order to support, e.g., anti-piracy protection. While waiting for such edge-cloud deployments to substantiate, even regional clouds could be utilized for the purpose. Were remote gaming approaches to utilize edge clouds, the games could be played without installing any infrastructure at the homes of end-users while keeping network delays to the latency-sensitive games low. At the same time, clouds are moving towards the end-users as " edge clouds " with different standardization bodies, such Open Mobile Edge Cloud and Open Fog Consortium, giving momentum for the efforts. In remote cloud gaming, the game is being executed and processed in the cloud while the user receives a video and audio stream of the game, in a very similar way as with remote desktop clients. For instance, Nvidia Shield, Valve Steam and Shinra technologies have offerings based on the concept. The thin-client approach for gaming is becoming more popular.
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