Cloud Gaming Systems Research Articles

An online cost optimization approach for edge resource provisioning in cloud gaming

Cloud gaming (CG), as an emergent computing paradigm, is revolutionizing the gaming industry. Currently, cloud gaming service providers (CGSPs) begin to integrate edge computing with cloud to provide services, with the aim of maximizing gaming service revenue while considering the costs incurred and the benefits generated. However, it is non-trivial to maximize gaming service revenue, as future requests are not known beforehand, and poor resource provisioning may result in exorbitant costs. In addition, the edge resource provisioning (ERP) problem in CG necessitates a trade-off between cost and inevitable queuing issues in CG systems. To address this issue, we propose ERPOL (ERP Online), a convenient and efficient approach to formulate ERP strategies for CGSPs, without requiring any future information. The performance of ERPOL has been theoretically validated and experimentally evaluated. Experiments driven by real-world traces show that it can achieve significant cost savings. The proposed approach has the potential to transform how CGSPs manage their infrastructure.

Traffic Reduction for Speculative Video Transmission in Cloud Gaming Systems

Traffic Reduction for Speculative Video Transmission in Cloud Gaming Systems

An architecture for multi-layer object coding in 2D game streaming using shared data in a multi-user environment

Through cloud computing, systems and services that offload game logic and rendering to a streaming server that provides content to a hardware-independent client are becoming more common. But there are still challenges on cloud gaming systems to keep low bandwidth usage and latency at acceptable rates. This work proposes a novel technique, called LACES (LAyer Caching gamE Streaming), to separate 2D game content into multiple layers that allows a server to encode and stream them separately, reusing workloads for multiple clients and updating only modified parts of a frame. We measured server workload, especially processing time spent on encoding tasks, stream bitrates and overall cache reuse. Using our methods, we were able to reduce encoding time in over 35% for cases with high number of layers and reduce stream bitrates in up to 90%, by having the client store a local client-side cache and update the layer data with small update packets, with no noticeable user input delay or impact on video resolution and quality. Finally, when allowing the cache generated at server-side to be reused for new users, we reduced encoding times to negligible amounts after the initial runs where the cache is being generated.

T-Gaming: A Cost-Efficient Cloud Gaming System at Scale

Cloud gaming (CG) system could pursue both high-quality gaming experience via intensive computing, and ultimate convenience anywhere at anytime through any energy-constrained mobile devices. Despite the abundance of efforts devoted, state-of-the-art CG systems still suffer from multiple key limitations: expensive deployment cost, high bandwidth consumption and unsatisfied quality of experience (QoE). As a result, existing works are not widely adopted in reality. This paper proposes a Transparent Gaming framework called T-Gaming that allows users to play any popular high-end desktop/console games on-the-fly over the Internet. T-Gaming utilizes the off-the-shelf consumer GPUs without resorting to the expensive proprietary GPU virtualization (vGPU) technology to reduce the deployment cost. Moreover, it enables prioritized video encoding based on the human visual feature to reduce the bandwidth consumption without noticeable visual quality degradation. Last but not least, T-Gaming adopts adaptive real-time streaming based on deep reinforcement learning (RL) to improve user's QoE. To evaluate the performance of T-Gaming, we implement and test a prototype system in the real world. Compared with the existing cloud gaming systems, T-Gaming not only reduces the expense per user by 75 percent hardware cost reduction and 14.3 percent network cost reduction, but also improves the normalized average QoE by 3.6-27.9 percent.

Improving Cloud Gaming Experience through Mobile Edge Computing

With the development of 4G/5G technology and smart devices, more and more users begin to play games via their mobile devices. As a promising way to enable users to play any games, cloud gaming is proposed to stream game scene rendered remotely in the cloud with the format of video. However, it faces major challenges in terms of long delay and high network bandwidth. To this end, a novel framework named EdgeGame is proposed to improve the cloud gaming experience by leveraging resources in the edge. Compared to existing cloud gaming systems, EdgeGame offloads the computation-intensive rendering to the network edge instead, which can reduce network delay and bandwidth consumption greatly. Moreover, EdgeGame introduces deep reinforcement learning in the edge to adjust the video bitrates adaptively to accommodate the network dynamics. Finally, we implemented a prototype system and compared it with an existing cloud gaming system. The experiments show that EdgeGame can reduce the average network delay by 50 percent and improve user's QoE by 20 percent.

Fine-Grained Scheduling in Cloud Gaming on Heterogeneous CPU-GPU Clusters

Cloud gaming is a promising approach to provide high-quality gaming services to mobile devices. However, existing cloud gaming systems fail to fully exploit hardware resources due to coarse-grained resource scheduling based on virtual machine migration. In this article, we propose a novel cloud gaming design, referred to as FGCG, with fine-grained scheduling to maximize resource utilization on a heterogeneous CPU-GPU cluster. Specifically, we decompose game workloads into small and independent render tasks that can be freely dispatched to different machines. Trace-driven simulation results show that FGCG can significantly improve resource utilization compared to existing cloud gaming systems.

Streaming Mobile Cloud Gaming Video Over TCP With Adaptive Source–FEC Coding

Cloud gaming has emerged as a promising application to enable high-end game playing with thin clients. Transmission control protocol (TCP) is pervasively adopted as the transport-layer protocol in the mainstream cloud gaming systems for video communication. However, streaming mobile cloud gaming video using the TCP is challenged with several key technical barriers: 1) the performance limitations of wireless networks in bandwidth and reliability; 2) the high throughput demand and stringent delay constraint imposed by high-quality gaming video transmission; and 3) the deadline violations and throughput fluctuations caused by the packet retransmission and congestion control mechanisms in the TCP. To address these critical problems, this paper proposes an application-layer source–forward error correction (FEC) coding framework dubbed adaptive source–FEC coding over TCP (ESCOT). First, we analytically formulate the optimization problem of joint source–FEC coding to minimize the end-to-end distortion of real-time video communication over TCP. Second, we develop a heuristic solution for effective loss rate approximation, source rate control, and FEC coding adaptation. ESCOT is distinct from existing source–FEC coding schemes in proactively analyzing and leveraging the TCP characteristics. The proposed solution is able to effectively mitigate both consecutive and sporadic video frame drops caused by congestion and random packet losses. We conduct the performance evaluation through extensive emulations in the Exata platform using real-time gaming video encoded by the H.264 codec. Experimental results show that the ESCOT advances the state of the art with noticeable improvements in video peak signal-to-noise ratio, end-to-end delay, goodput, and frame success rate.

LiveRender: A Cloud Gaming System Based on Compressed Graphics Streaming

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server’s burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth-consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open-source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intraframe compression, interframe compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52%–73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared to the video streaming approach, LiveRender achieves a traffic reduction of 40%–90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

Dynamic Bin Packing for On-Demand Cloud Resource Allocation

Dynamic Bin Packing (DBP) is a variant of classical bin packing, which assumes that items may arrive and depart at arbitrary times. Existing works on DBP generally aim to minimize the maximum number of bins ever used in the packing. In this paper, we consider a new version of the DBP problem, namely, the MinTotal DBP problem which targets at minimizing the total cost of the bins used over time. It is motivated by the request dispatching problem arising from cloud gaming systems. We analyze the competitive ratios of the modified versions of the commonly used First Fit, Best Fit, and Any Fit packing (the family of packing algorithms that open a new bin only when no currently open bin can accommodate the item to be packed) algorithms for the MinTotal DBP problem. We show that the competitive ratio of Any Fit packing cannot be better than $\mu +1$ , where $\mu$ is the ratio of the maximum item duration to the minimum item duration. The competitive ratio of Best Fit packing is not bounded for any given $\mu$ . For First Fit packing, if all the item sizes are smaller than $\frac{1}{\beta }$ of the bin capacity ( $\beta >1$ is a constant), the competitive ratio has an upper bound of $\frac{\beta }{\beta -1}\cdot \mu + \frac{3\beta }{\beta -1}+1$ . For the general case, the competitive ratio of First Fit packing has an upper bound of $2\mu + 7$ . We also propose a Hybrid First Fit packing algorithm that can achieve a competitive ratio no larger than $\frac{5}{4}\mu + \frac{19}{4}$ when $\mu$ is not known and can achieve a competitive ratio no larger than $\mu + 5$ when $\mu$ is known.

Playing High-End Video Games in the Cloud: A Measurement Study

Cloud gaming has emerged as a promising approach to more affordable and accessible games. By rendering high-end video games in the cloud and streaming encoded game scenes to players via the broadband networks, users are relieved from downloading or installing game software. With cloud gaming, users can easily play high-end 3-D video games on any devices anytime and anywhere. In this paper, we conducted a comprehensive measurement study of a leading cloud gaming system in China, namely, CloudUnion. Unlike the previous work, our measurement study was based on an in-depth understanding of the internal mechanisms of CloudUnion, and thus we were able to reveal problems that cannot be observed in a black-box approach. We built a dedicated measurement platform, which enables us to study CloudUnion from different views, including the global view, local view, and user view. We also conducted a comparison study with another cloud gaming system, namely, GamingAnywhere. Our measurement results unveil the pros and cons of the current cloud gaming system design, and bring forth important insights about the cloud infrastructure, user behaviors, traffic patterns, user-perceived quality, and so on. Our work will be valuable for the design of future cloud gaming systems.

Quality-of-Experience Optimization for a Cloud Gaming System With <italic>Ad Hoc</italic> Cloudlet Assistance

Cloud gaming systems host the game in the cloud, while Gameplays and views are streamed to the players’ terminals in the form of encoded video frames. To address the high-bandwidth issue of real-time gaming video transmission, we have proposed a cloudlet-assisted multiplayer cloud gaming system to encourage cooperative video sharing, which exploits the similarities of video frames among multiple players in the same crowd playing the same game via a secondary ad hoc network. In this paper, we provide a detailed modeling of the proposed system, including the correlation between video frames, mobility of terminal devices, and diversity of network quality of service for distinct players. With necessary mathematical formulations, we study the players’ behaviors regarding the cooperative sharing patterns to optimize the system performance in terms of the quality of users’ experience. Also, heuristic algorithms are proposed to reduce the computational complexity. Empirical study and trace-driven simulation results illustrate the impact of mobility on the system performance and show that the proposed solution is able to provide better quality of experience compared with the existing platform.

Enabling Adaptive High-Frame-Rate Video Streaming in Mobile Cloud Gaming Applications

High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme’s capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems.

Introduction to the Special Section on Visual Computing in the Cloud: Cloud Gaming and Virtualization

Cloud gaming, the newest entry in the online gaming world, leverages the well-known concept of cloud computing to provide real-time gaming services to players. The idea in cloud gaming is to capture the game events from players and transmit them to the cloud, process those events and run the game logic in the cloud, render the game scene as video in the cloud, and stream that video to the players. The advantage is that as long as the client can display video, which pretty much all smartphones, tablets, game consoles, desktops, laptops, and mobile devices today do, the user can play the game without installing it locally, and without needing to have a machine with high-grade 3-D graphics rendering and powerful computing hardware and software. This makes cloud gaming accessible to a huge market of mass consumers. While some variations of cloud gaming systems stream 3-D graphics, in addition to or instead of video, the great majority of cloud gaming implementations are video based. Using the well-known concept of software as a service, cloud gaming is also sometimes referred to as gaming as a service, which is already available as commercial products, such as Sony’s PlayStation Now, Ubitus’s GameNow, G-Cluster, Crytek’s GFACE, PlayGiga, and LiquidSky, to name a few. There are also many efforts concentrating specifically on the underlying technology behind cloud gaming, such as NVIDIA’s Grid, OTOY, CiiNow, Kalydo, and GamingAnywhere [4], the latter being the only open source and free technology. Microsoft is also exploring cloud gaming technologies, with recent successes such as its Kahawai project [1].

On the Quality of Service of Cloud Gaming Systems

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.

GamingAnywhere

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.

Network Traffic Adaptation for Cloud Games

With the arrival of cloud technology, game accessibility and ub iquity have a bright future; Games can be hosted in a centralize server and accessed through the Internet by a thin client on a wide variety of devices with modest capabilities: cloud gaming. However, current cloud gaming systems have very strong requirements in terms of network resources, thus reducing the accessibility and ubiquity of cloud games, because devices with little bandwidth and people located in area with limitedand unstable network connectivity, cannot take advantage of these cloud services. In this paper we present an adaptation technique inspired by the level of detail (LoD) approach in 3D graphics. It delivers multiple platform accessibility and network adaptability, while improving user’s quality of experience (QoE) by reducing the impa ct of poor and unstable network parameters (delay, packet loss, jitter) on game interactivity. We validate our approach using a prototype game in a controlled environment and characterize the user QoE in a pilot experiment. The results show that the propos ed framework provides a significant QoE enhancement.