A Predictive Frame Transmission Scheme for Cloud Gaming in Mobile Edge Cloudlet Systems

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
$1\%$
.