Cloud gaming is promising yet poses big challenges to wireless communications, due to its stringent requirements for low response delay and high reliability. In this paper, we propose a predictive frame transmission scheme (PFT) in cloud gaming, to predict and pre-transmit future game frames to users. The PFT scheme takes full advantage of good network states to transmit the predicted frames, which consequently reduces the frame loss rate (FLR) against the network dynamics. We first model a FLR minimization problem in the single-user system with the PFT scheme, which allocates packets to carry the predicted frames. The upper and lower bounds of FLR are derived, respectively. Then, we study the system with Markovian property, and derive the optimal packet allocation policy via Markov Decision Process. A near-optimal policy is also proposed with low-complexity. The PFT scheme is further extended to the multi-server multi-user scenario, in which the users are adaptively scheduled to multiple servers based on their different requirements. Finally, we extend the policy to fit the scenario without direct knowledge of the network state by exploiting the packet loss rate estimation. We set up a practical testbed to evaluate the proposed PFT scheme, showing the capability of decreasing the mean FLR from$7\%$to$1\%$.

A promising research area that has recently emerged, is on how to use index coding to improve the communication efficiency in distributed computing systems, especially for data shuffling in iterative computations. In this paper, we posit that pliable index coding can offer a more efficient framework for data shuffling, as it can better leverage the many possible shuffling choices to reduce the number of transmissions. We theoretically analyze pliable index coding under data shuffling constraints, and design a hierarchical data-shuffling scheme that uses pliable coding as a component. We find benefits up to O(ns/m) over index coding, where ns/m is the average number of workers caching a message, and m, n, and s are the numbers of messages, workers, and cache size, respectively.

Mobile edge computing is a novel technique in which mobile devices offload computation-intensive tasks with stringent delay requirements to the edge cloud. However, the limited computational resource in the edge cloud may result in the Quality of Service degradation. In this paper, we address this issue by coordinating the heterogeneous cloud which includes the edge cloud and the remote cloud. Considering the offloading of delay-bounded tasks, we study into the scheduling of heterogeneous cloud in order to maximize the probability that tasks can have the delay requirements met. The problem formulation is proved to be concave, and an optimal algorithm is proposed accordingly. The optimal policy with heterogeneous cloud is notably different from the policy merely using the edge cloud. With only the edge cloud, the system serves tasks with loose delay bounds and drops tasks with stringent delay bounds when the traffic load is heavy. However, with the heterogeneous cloud, tasks with stringent delay bounds are offloaded to the edge cloud and tasks with loose delay bounds are offloaded to the remote cloud. In numerical results, the probability that the delay bounds of tasks are satisfied can be improved by about 40% with the assistance of the remote cloud.

A promising area that has recently emerged, is on how to use index coding to improve the communication efficiency in distributed computing systems, especially for data shuffling in iterative computations. In this paper, we posit that pliable index coding can offer a more efficient framework for data shuffling, as it can better leverage the many possible shuffling choices to reduce the number of transmissions. We theoretically analyze pliable index coding under data shuffling constraints, and design an hierarchical data-shuffling scheme that uses pliable index coding as a component. We find transmission benefits up to O(ns/m) over index coding, where ns/m is the average number of workers caching a message, and m, n, and s are the numbers of messages, workers, and cache size, respectively.

Edge cloud is a promising architecture in order to address the latency problem in mobile cloud computing. However, as compared with remote clouds, edge clouds have limited computational resources, and higher operating costs. In this paper, we design policies which carry out the assignment of tasks that are generated at the mobile subscribers with edge clouds in an online fashion. The proposed policies achieve an optimal power-delay trade-off in the system. Here, the delay experienced by a mobile computing task includes the time spent waiting for transmission to the edge cloud, and the execution time at the edge cloud servers. We perform a theoretical analysis after modeling the system as a continuous-time queueing system. The contribution of this paper is two-fold: Firstly, the algorithm to determine the optimal policy is obtained by proposing an equivalent discrete-time Markov decision process. Secondly, an easily implementable index policy is proposed by analyzing the dual of the original problem. Extensive simulations illustrate the effectiveness of the proposed policies.