The rapidly evolving technology of edge computing shows great potential in revolutionizing the market of cloud gaming. Edge computing can significantly lower the latency for better gaming experiences by performing computation at the proximity of game players. However, the acceleration of latency-sensitive cloud gaming services at the edge is challenging due to the heterogeneity of edge servers, different requirements among game players, and so on. In this paper, we propose an efficient latency-sensitive scheduling algorithm called EGSA to satisfy latency constraints for cloud gaming services at the edge. We first formulate the problem as a weighted congestion game, which takes a number of key factors (e.g., game genres, user strategies, latency constraint and device heterogeneity) into account. Based on the weighted congestion game model, we further design an efficient latency-sensitive scheduling algorithm, which can approximate the pure Nash equilibrium under Shapley cost-sharing method. We also perform theoretic analysis to prove that our proposed algorithm converges in polynomial steps. Finally, we conduct a set of experiments and the results show that our algorithm outperforms alternative strategies with up to 46% performance improvement.

With the development of smart cities, Internet services will be pervasively accessible for moving vehicles. It is envisioned that the video content demand of vehicles will explode in the near future. However, the strategy to efficiently distribute video content in large-scale vehicular networks is still absent due to challenges arising from the huge video population, heavy bandwidth consumption, heterogeneous user devices, and vehicles’ mobility. In this work, we propose to collaboratively replicate video content on Roadside Units (RSUs) to enhance video distribution services based on the fact that the contact period between moving vehicles and a single RSU is not long enough to complete video downloading. In our design, a video file is split into multiple chunks. Each RSU replicates a small number of original chunks and chunks encoded by network coding. Replicating encoded chunks can reduce redundancy of chunks on different RSUs so that RSUs can complement each other better, whereas original chunks can be transrated to chunks with lower bitrates flexibly to fit in users’ devices. Therefore, we replicate both original and encoded chunks on RSUs to take advantages of both sides. Stochastic models are employed to analyze chunk download processes and a convex optimization problem is formulated to determine the optimal partition of space allocated to each kind of chunks. Furthermore, we extend our strategy to support video streaming services and empirically prove that the influence caused by limitations of network coding is moderate. In the end, we conduct extensive simulations which not only validate the accuracy of our models but also demonstrate that our strategy can effectively boost video distribution services.

With the advance of cloud computing and 4G/5G technology, video contents recorded by in-car cameras (i.e., vehicular digital video recorders) can be uploaded to the cloud to facilitate accident analysis, online surveillance, video sharing, etc. However, the cost of uploading such huge volume of video contents via unstable vehicular access networks (including cellular base stations and road-side units) can be considerable by considering the increasing video quality requirement, time constraint, and limited local buffer space. In this paper, we propose an adaptive video recording and uploading scheme to maximize the overall utility of cloud-based in-car video uploading over vehicular access networks. Specifically, the utility function is defined as the weighted sum of bandwidth cost and video quality and we formulate the problem into a constrained Markov decision process (MDP). Based on the theoretic foundation of MDP, we design and implement an algorithm to obtain an adaptive chunk uploading policy for video contents over vehicular access networks. Extensive simulations have been conducted to demonstrate that our policy can achieve the best performance compared with other alternative strategies.

Recently, crowdsourcing-based content delivery networks (CDN) emerge as a promising technology that can distribute massive video content to a vast number of Internet users by crawling bandwidth and storage resources from Internet end devices. Any ordinary Internet users with excessive resources can be recruited into such systems as mini-servers. Different from edge servers equipped with dedicated resources in traditional CDNs, the resource of a single mini-server is scarce and volatile that can vary severely with time, since its bandwidth is shared by many different applications. How to build a robust high performance crowdsourcing-based CDN system has attracted contributions from both academia and industry, but how to solve the drawback caused by unstable uploading bandwidth is still a challenging problem. So far, a prevalent methodology is to migrate the strategies implemented by traditional CDNs into crowdsourcing-based CDN systems based on the fact that these two kinds of systems share many similarities. In this paper, our argument is that the content delivery time can be reduced by replicating coded content on mini-servers (which is almost useless for edge servers in traditional CDNs) to enable downloading users to automatically adapt their downloading progress with oscillating bandwidth capacity from different mini-servers. Theoretical model is created to derive the performance improvement (evaluated in term of average file downloading time) achieved by our strategy, which is further validated via simulation. This paper not only provides system designers a more efficient content replication solution, but also can push forward the development of the crowdsourcing-based CDNs.