Applying cloud technology to 3D interactive multimedia applications is a promising way to provide flexible and cost-efficient online high-bandwidth immersive services to a large population of end users. One main reason cloud systems are popular among users is the fact that it relaxes the hardware requirements for high-end interactive visual applications. As most of the computational tasks are done on cloud servers, users no longer need to upgrade their hardware as frequently to keep up with the ever-increasing high-end computing requirements of the latest applications. Moreover, cloud systems make it easier for a user to enjoy applications on different platforms, including mobile devices that are usually not powerful enough to run high-end, memory-intensive services. In short, applying cloud technology to high-end immersive applications has advantages in cost-efficiency and flexibility both for the end users and the service providers. However, there are two main drawbacks to applying cloud technology to 3D interactive multimedia services: (1) high-bandwidth utilization and (2) latency. In this article, we propose a framework that addresses the two problems for single-player cloud gaming by using a combination of collaborative rendering, progressive meshes, and 3D image warping techniques. The experimental results show that the proposed system can reduce the bandwidth usage and improve the visual quality by utilizing local computing power on the client. The results also show that the interaction latency can be reduced somewhat by sacrificing some degree of visual quality in the end system.

Keyword-based heat maps are a natural way to explore and analyze the spatial properties of social media data. Dealing with large datasets, there may be many different keywords, making offline pre-computations very hard. Interactive frameworks that exploit database sampling can address this challenge. We present a novel middleware technique called Heatflip, which issues diametrically opposed samples into the temporal and spatial dimensions of the data stored in an external database. Spatial samples provide insights into the temporal distribution and vice versa. The progressive exploration approach benefits from adaptive indexing and combines the retrieval and visualization of the data in a middleware layer. Without any a priori knowledge of the underlying data, the middleware can generate accurate heat maps in 85% shorter processing times than conventional systems. In this paper, we discuss the analytical background of Heatflip, showcase its scalability, and validate its performance when visualizing large amounts of social media data.

Optimizing cloud gaming experience is no easy task due to the complex tradeoff between gamer quality of experience (QoE) and provider net profit. We tackle the challenge and study an optimization problem to maximize the cloud gaming provider's total profit while achieving just-good-enough QoE. We conduct measurement studies to derive the QoE and performance models. We formulate and optimally solve the problem. The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services with dedicated infrastructures, where the profit is not a concern and overall gaming QoE needs to be maximized. We present a prototype system and testbed using off-the-shelf virtualization software, to demonstrate the practicality and efficiency of our algorithms. Our experience on realizing the testbed sheds some lights on how cloud gaming providers may build up their own profitable services. Last, we conduct extensive trace-driven simulations to evaluate our proposed algorithms. The simulation results show that the proposed heuristic algorithms: (i) produce close-to-optimal solutions, (ii) scale to large cloud gaming services with 20,000 servers and 40,000 gamers, and (iii) outperform the state-of-the-art placement heuristic, e.g., by up to 3.5 times in terms of net profits.

Displays without associated computing devices are increasingly more popular, and the binding between computing devices and displays is no longer one-to-one but more dynamic and adaptive. Screencast technologies enable such dynamic binding over ad hoc one-hop networks or Wi-Fi access points. In this paper, we design and conduct the first detailed measurement study on the performance of state-of-the-art screencast technologies. By varying the user demands and network conditions, we find that Splashtop and Miracast outperform other screencast technologies under typical setups. Our experiments also show that the screencast technologies either: (i) do not dynamically adjust bitrate or (ii) employ a suboptimal adaptation strategy. The developers of future screencast technologies are suggested to pay more attentions on the bitrate adaptation strategy, e.g., by leveraging cross-layer optimization paradigm.

Cloud gaming, i.e., real-time game playing via thin clients, relieves users from being forced to upgrade their computers and resolve the incompatibility issues between games and computers. As a result, cloud gaming is generating a great deal of interests among entrepreneurs, venture capitalists, general publics, and researchers. However, given the large design space, it is not yet known which cloud gaming system delivers the best user-perceived Quality of Service (QoS) and what design elements constitute a good cloud gaming system. This study is motivated by the question: How good is the QoS of current cloud gaming systems? Answering the question is challenging because most cloud gaming systems are proprietary and closed, and thus their internal mechanisms are not accessible for the research community. In this paper, we propose a suite of measurement techniques to evaluate the QoS of cloud gaming systems and prove the effectiveness of our schemes using a case study comprising two well-known cloud gaming systems: OnLive and StreamMyGame. Our results show that OnLive performs better, because it provides adaptable frame rates, better graphic quality, and shorter server processing delays, while consuming less network bandwidth. Our measurement techniques are general and can be applied to any cloud gaming systems, so that researchers, users, and service providers may systematically quantify the QoS of these systems. To the best of our knowledge, the proposed suite of measurement techniques have never been presented in the literature.

We present the first open source cloud gaming system, called GamingAnywhere. In addition to its openness, we have designed, GamingAnywhere for high extensibility, portability, and reconfigurability. We implemented it on Windows, Linux, OS X, and Android. We conducted extensive experiments to evaluate its performance. Our experimental results indicate that GamingAnywhere is efficient, scalable, adaptable to network conditions, and achieves high responsiveness and streaming quality. GamingAnywhere can be employed by researchers, game developers, service providers, and end users for setting up cloud gaming testbeds, which we believe, will stimulate more research into innovations for cloud gaming systems and applications.

While cloud gaming opens new business opportunity, it also poses tremendous challenges as the Internet only provides best-effort service and gamers are hard to please. Although researchers have various ideas to improve cloud gaming systems, existing cloud gaming systems are closed and proprietary, and cannot be used to evaluate these ideas. We present GamingAnywhere, the first open-source cloud gaming system, which is extensible, portable, and configurable. GamingAnywhere may be used by: (i) researchers and engineers to implement and test their new ideas, (ii) service providers to develop cloud gaming services, and (iii) gamers to set up private cloud gaming systems. Details on GamingAnywhere are given in this paper. We firmly believe GamingAnywhere will stimulate future studies on cloud gaming and real-time interactive distributed systems.

This paper introduces techniques in scheduling jobs on a master/workers platform where the bandwidth is shared by all workers. The goal is to minimize the total makespan. The jobs are independent and each job requires a fixed amount of bandwidth to download input data before execution. The master can communicate with multiple workers simultaneously, provided that the bandwidth used by the master and the workers do not exceed their bandwidth limits. We proposed two models for this limited-bandwidth problem. If the data transfer cannot be interrupted, then we prove that the scheduling problem is NP-complete. Nevertheless we propose heuristic algorithms and experimentally test their performance. If the data transfer can be interrupted, we propose an algorithm that produces optimal makespan. The algorithm is based on a binary search on the completion time, and an efficient feasibility verification process for a given completion time.