Geographic routing has been considered as an attractive approach for wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, it often suffers from the routing hole in various environments during data delivery, resulting in route failure. Currently, existing geographic routing tends to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, they cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we propose an energy-aware dual-path geographic routing (EDGR) to address these issues. EDGR utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into 3D WSNs to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR is superior to existing geographic routing in terms of energy efficiency, network lifetime, packet deliver ratio and delivery delay in WSNs over a variety of communication sessions passing through routing holes.

The 360-degree video streaming has higher bandwidth requirements compared with traditional video to achieve the same user-perceived playback quality. Since users only view part of the entire videos, viewport-adaptive streaming is an effective approach to guarantee video quality. However, the performance of viewport-adaptive schemes is highly dependent on the bandwidth estimation and viewport prediction. To overcome these issues, we propose a novel reinforcement learning (RL) based viewport-adaptive streaming framework called RLVA, which optimizes the 360-degree video streaming in viewport prediction, prefetch scheduling and rate adaptation. Firstly, RLVA adopts t location-scale distribution rather than Gaussian distribution to describe the viewport prediction error characteristic more accurately and achieve the tile viewing probability based on the distribution. Besides, a tile prefetch scheduling algorithm is proposed to update the tiles according to the latest prediction results, which further reduces the adverse effect of prediction error. Furthermore, the tile viewing probabilities are treated as input status of RL algorithm. In this way, RL can adjust its policy to adapt to both of the network conditions and viewport prediction error. Through extensive evaluations, the simulation results show that the proposed RLVA outperforms other viewport-adaptive methods by about 4.8%-66.8% improvement of Quality of Experience (QoE) and effectively reduces the impact of viewport prediction errors.

With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching schemes do not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve this problem, we design a new learning-based cooperative caching strategy (LECS) that encompasses three key components. Firstly, a temporal convolutional network driven content popularity prediction model is developed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content cached on a given edge server. Thirdly, an novel dynamic programming algorithm is developed to maximize the overall CCV. Extensive simulation results have demonstrated the superiority of our approach. Compared with the state-of-the-art caching schemes, LECS can improve the cache hit rate by 8.3%-10.1%, and reduce the average content delivery delay by 9.1%-15.1%.

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% hardware cost reduction and 14.3% network cost reduction, but also improves the normalized average QoE by 3.6%-27.9%.

To bridge the gap of conventional single-hop task offloading schemes in infrastructure-free scenarios, multi-hop task offloading schemes for IoT devices in Mobile Edge Computing (MEC) are desired to jointly optimize task offloading decisions and routing paths. In this paper, we investigate a hierarchical multi-hop edge computing framework and propose a joint Task Offloading and Relay Selection (TORS) scheme. It considers real-time computation at each relay node and employs directional searches to facilitate the task execution and results reporting at the fastest speed. However, finding the optimal TORS solution is a formidable challenge due to the time-varying network environments, the strong interdependence of decision sets across different time slots, and the high computational complexity. To address these challenges, we first leverage Lyapunov optimization to transform the stochastic TORS problem into a deterministic per-slot block problem, avoiding the need for extensive system prior knowledge. Subsequently, we propose a Soft Actor-Critic (SAC)-based algorithm, SAC-TORS, to find a satisfactory TORS solution with minimal computational complexity in a distributed manner. Accordingly, each IoT device can independently make self-determined and directional decisions with observable network information. Through extensive experiments, we demonstrate that the SAC-TORS outperforms state-of-the-art solutions, achieving performance improvements of up to 66%.

Remote Direct Memory Access (RDMA) has been widely deployed in datacenters for its high performance. Large-scale high performance cloud services built on geographically distributed datacenters require long-range RDMA for performance requirements. However, existing RDMA solutions can hardly satisfy the stringent requirements of the emerging large-scale high-performance cloud services built on geo-distributed datacenters in terms of throughput and delay. On the one hand, lossless RDMA suffers from a deep buffer and potential suboptimal throughput for inter-datacenter traffic due to delayed response to Priority Flow Control (PFC) messages. On the other hand, lossy RDMA with selective retransmissions suffers from poor performance when multiple flows with different round-trip times (RTTs) coexist in cross-datacenter scenarios. This article proposes Swing , which expands the high-performance lossless RDMA to long-distance links through PFC-Relay. Swing ensures the throughput of long-distance links while minimizing the buffer requirement for long-range RDMA. It enables long-range RDMA without making any modifications to existing in-datacenter networks. The evaluation shows that Swing can reduce the average flow completion time (FCT) by 14%-66% in a variety of traffic scenarios.