Wireless edge networks are promising to provide better video streaming services to mobile users by provisioning computing and storage resources at the edge of wireless network. However, due to the diversity of user interests, user devices, video versions or resolutions, cache sizes, network conditions, etc., it is challenging to decide where to place the video contents, and which cache and video version a mobile user device should select. In this paper, we study the joint optimization of cache-version selection and content placement for adaptive video streaming in wireless edge networks. We propose practical distributed algorithms that operate at each user device and each network cache to maximize the overall network utility. In addition to proving the optimality of our algorithms, we implement our algorithms as well as several baseline algorithms on ndnSIM, an ns-3 based Named Data Networking simulator. Simulation evaluations demonstrate that our algorithms significantly outperform conventional heuristic solutions.

It has been shown that it is impossible to achieve stringent timely delivery guarantees in a large network without having complete information of all future packet arrivals. In order to maintain desirable performance in the presence of uncertainty of future, a viable approach is to add redundancy by increasing link capacities. This paper studies the amount of capacity needed to provide stringent timely delivery guarantees. We propose a low-complexity online algorithm and prove that it only requires a small amount of redundancy to guarantee the timely delivery of most packets. Furthermore, we show that in large networks with very high timely delivery requirements, the redundancy needed by our policy is at most twice as large as the theoretical lower bound. For practical implementation, we propose a distributed protocol based on this centralized policy. Without adding redundancy, we further propose a low-complexity order-optimal online policy for the network. The simulation results show that our policies achieve much better performance than the other state-of-the-art policies.

Edge servers, which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile Internet applications by moving cloud services to the edge of the network. Due to limited capacity of edge servers and dynamic request arrival, proper service caching at the edge is essential to guarantee good performance. This paper proposes a tractable online algorithm called retrospective download with least-requested deletion that caches services dynamically without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio of our policy, which quantifies the worst case performance in comparison to an optimal offline policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge server. We also show that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. Moreover, we prove that our policy is scalable, in the sense that it only needs doubled capacity to achieve a constant competitive ratio. The utility of our online policy is further evaluated on real-world traces. These trace-based simulations demonstrate that our policy has better, or similar, performance compared with many intelligent offline policies.

This paper proposes a practical non-monetary mechanism that induces the efficient solution to the optimal rate control problem, where each client optimizes its request arrival rate to maximize its own net utility individually, and at the Nash Equilibrium the total net utility of the system is also maximized. Existing mechanisms typically rely on monetary exchange which requires additional infrastructure that is not always available. Instead, the proposed protocol is based on efficient delay allocation, where the server controls the delay experienced by each client through an intelligent scheduling policy. Specifically, we present an efficient delay allocation rule for the server to determine the target delay of each client. Then we propose a simple scheduling policy to achieve such delay allocation. Furthermore, we design a distributed rate control protocol for the system to converge to the Nash Equilibrium. The optimality of our mechanism is validated via extensive simulations on two representative systems against a baseline mechanism with FIFO scheduling and centralized rate control.

"Edge-clouds," which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile applications by moving cloud services closer to users at the edge. Due to their limited storage capacity, proper configurations of edge-clouds have a significant impact on their performance. This paper proposes a tractable online algorithm that configures edge-clouds dynamically solely based on past system history without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio, which quantifies the worst-case performance in comparison to an optimal offline policy, of our policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge-cloud. Moreover, we also prove that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. The utility of our online policy is further evaluated by traces from real-world data centers. These trace-based simulations demonstrate that our policy has better, or similar, performance compared to many intelligent offline policies that have complete knowledge of all future arrivals.