In mobile and wireless networks, controlling data delivery latency is one of open problems due to the stochastic nature of wireless channels, which are inherently unreliable. This paper explores how the current best-effort throughput-oriented wireless services might evolve into latency-sensitive enablers of new mobile applications such as remote three-dimensional (3D) graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the standard latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible delay of data re-ordering, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse data delivery over multiple paths with uncorrelated outage likelihoods; and (ii) forward packet-loss protection (FPP), creating encoding redundancy for proactive recovery of intolerably delayed data without end-to-end retransmissions. Analysis and evaluation are based on traces of real life traffic, which is measured in live carrier-grade long term evolution (LTE) networks and campus WiFi networks, due to no such system/environment yet to verify the importance of spatial diversity and encoding redundancy. Analysis and evaluation reveal the seriousness of the latency problem and that the proposed FPP with spatial diversity and encoding redundancy can minimize the delay of re-ordering. Moreover, a novel FPP effectiveness coefficient is proposed to explicitly represent the effectiveness of EPP implementation.

Buffer management and delivery latency in various networks have been extensively studied. However, little work has considered the condition in which the traffic exhibits interpacket dependency, a common occurrence with many applications. Furthermore, the existing work related to such traffic mainly focuses on maximizing goodput and little attention has been paid to delivery latency. This paper concentrates on the delivery latency minimization problem for streaming data with packet dependencies. A novel optimization model is proposed to describe the aforementioned problem and the theoretical lower bound for delivery latency is deduced. Based on this model, a plain buffer management (PBM) algorithm is applied to the implementation of the buffer scheduling process. Afterwards, we improve the PBM algorithm under the guidance of a heuristic idea and put forward an optimal buffer management greedy (OBMG) algorithm. Experiments demonstrate that the OBMG algorithm outperforms the currently best known online (BKO) algorithm as it decreases the average delivery latency by 35.6%. In some cases, delivery latency obtained from the OBMG algorithm can be quite close to the theoretical lower bound. In addition, the OBMG algorithm can reduce CPU computational overhead by more than 12% in comparison to the BKO algorithm.

Delay Tolerant Network (DTN) has been emerged to support the network connectivity of the disruptive networks. A variety of routing methods have been proposed to reduce the latency for message delivery. PROPHET was proposed as a probabilistic routing that utilizes history of encounters and transitivity of nodes, which is computed as contact probability. While PROPHET improves the performance of DTN due to contact probability, contact probability is just one parameter reflecting the mobility pattern of nodes, and further study on utilizing contacting information of mobility pattern is still an important problem. Hence, in this paper, we try to improve routing for DTN by using a novel metric other than contact probability as mobility information. We propose the routing protocol to use mean residual contact time that describes the contact period for a given pair of nodes. The simulation results show that using the mean residual contact time can improve the performance of routing protocols for DTN. In addition, we also show in what situations the proposed method provides more efficient data delivery service. We characterize these situations using a parameter called Variation Metric.