In pursuit of low latency, real-time communication (RTC) service providers usually use multi-hop overlay links worldwide to bypass congested links, especially for medium- and long-distance transmissions. In such multi-hop long-distance transmission scenarios, utilizing retransmission to recover lost packets can result in increased end-to-end latency. Therefore, Forward Error Correction (FEC) is viewed as a promising way to solve the loss problem. However, for multi-hop overlay transmission, existing FEC schemes either introduce a non-negligible processing delay at each hop or reduce the processing delay at the cost of a high coefficient overhead. In this work, we propose a multi-hop FEC scheme, i.e., FEC-OEM, which considers both processing delay and coefficient overhead. FEC-OEM is designed based on two observations we obtained from measurements. First, coefficient overhead can only be reduced through an implicit transmission way. Therefore, we design a modulation-based recoding module that enables implicit coefficient transmission and hop-by-hop recoding at the same time. Second, using on-the-fly computation is a promising way to reduce processing delay. Accordingly, we design an elimination method to make the modulation-based recoding can be carried out on-the-fly. Real-world experiments demonstrate that FEC-OEM can reduce the processing delay by up to 88% without increasing the coefficient overhead compared to state-of-the-art schemes. We also use FEC-OEM to transmit packets for applications with different loss tolerances, and the results show that FEC-OEM can improve the QoE more effectively than state-of-the-art coding schemes.

Forward Error Correction (FEC) is the preferred method for recovering lost packets in time-sensitive applications. The key for FEC to recover lost packets successfully is whether the number of redundant packets is sufficient. Due to imperfect loss prediction algorithms, existing FEC schemes, which set the number of redundant packets according to the prediction results, make transport service providers likely to fall into the awkward situation of either failing to recover lost packets or wasting a large amount of bandwidth. In this work, we propose P-FEC, an FEC scheme that can take SLA into consideration and empirically achieve the targeted decoding success rate while minimizing bandwidth waste. P-FEC combines intra- and inter-generation coding to balance decoding success rate and bandwidth waste, in which intra-generation provides quick but conservative recovery and inter-generation coding provides delayed but more efficient loss recovery services. We future profile the loss prediction errors to derive cumulative distribution functions of the errors for diverse network conditions, and then determine the parameters of intra- and inter-generation coding according to these distribution functions. The real-world transmission experiments empirically demonstrate that P-FEC can achieve the targeted decoding success rate while its bandwidth waste is only 1%-16% of the FEC with the code rate that can theoretically guarantee the target success rate. Furthermore, P-FEC can work well with computation light-weighted prediction algorithms although these algorithms have low accuracy, which makes it extremely useful for the transmission environment with limited computing resources.

Forward Error Correction (FEC) is the preferred method for recovering lost packets in time-sensitive applications. However, setting the code rate to adapt to changing network environment is still a significant but challenging problem. Recently, researchers explored deep learning (DL) methods to solve this problem, but DL-based methods come with increased computation overhead and decision-making latency compared to traditional methods, which may make these methods infeasible or less effective. Thus, real-time network (RTN) providers usually have trouble with these two questions when considering whether to adopt a DL-based method: (1). How much additional computing resources are required by each DL-based method? (2). If resource competition occurs, can these DL-based methods be able to make timely code rate decisions? In this paper, we evaluate existing proposed DL-based methods for code rate determination in terms of computational cost to answer these two questions. Particularly, we measure the computational resources and the time required by these DL-based methods for making one code rate decision. We find that among existing DL-based methods, some methods require several times or even tens of times more computational resources to achieve timely decision-making. Furthermore, we identify that three methods are prone to resource competition with existing FEC schemes, which indicates that RTN providers need to configure their computing resources carefully when selecting these methods. We further analyze the reasons behind this phenomenon and provide some design suggestions for RTN nroviders.

Due to the high cost of cellular networks, vehicle users would like to offload elastic traffic through vehicular networks as much as possible. This demand prompts researchers to consider how to make the vehicular network system achieve better performance for requests coming online, such as maximizing throughput. The traffic in vehicular networks is transferred through opportunistic contacts between vehicles and infrastructures. When making scheduling decisions, the scheduler must be aware of vehicles’ future trajectories. Vehicles’ future trajectories are usually predicted by trajectory prediction algorithms when users are unwilling to report their future trips. Unfortunately, no trajectory prediction algorithm can be completely accurate, and these inaccurate prediction results will degrade the throughput achieved by scheduling algorithms. In this paper, we focus on reducing the negative impact of inaccurate predictions. Specifically, we measure two data-driven trajectory prediction algorithms that have been widely used for trajectory predictions and understand the characteristics of the accuracy of predicted contacts. Based on the enlightenment from the measurement, we design a system, i.e., i-Offload, to offload elastic traffic under imperfect trajectory predictions. The experimental results show that our system has good throughput and high scheduling efficiency even under imperfect trajectory predictions. Compared with existing scheduling algorithms, our method improves the throughput by about one time.

Overlay routing control for live video delivery has become an important yet challenging task for Real-Time Networks (RTNs), but existing approaches designed for traditional Content Delivery Networks (CDNs) fall short of meeting the challenge. In this paper, we develop a distributed overlay routing controller for RTNs to deliver massive high-quality live video streams in low latencies. We first formulate a joint optimization that offers rich control flexibility and can yield low-latency and cost-effective routing solutions. To obtain the appealing potential value of the optimal solution in the context of large-scale live videos, we develop a distributed control framework that can find near-optimal solutions at fine-grained timescales. Evaluations on real-world live video traces show that our distributed controller derives high-quality (in terms of both performance and cost) overlay routing solutions while reducing the decision latency by 38%-89% compared to the state-of-the-art centralized controller.

DHCP is widely used to dynamically allocate IP addresses to the devices on local area networks, but the explosive increases of WiFi devices and their frequent mobility pose great challenges on DHCP performance in wireless LANs. In this paper, by analyzing large scale real network traces, we observe that the dynamic WiFi user behavior (e.g., online time pattern and spatio-temporal mobility pattern) leads to the poor DHCP performance. The IP pools in some VLANs have been exhausted in rush hours although the total IP utilization in WLAN is only 24%. Therefore, we have to configure IP lease times and IP pools dynamically and make sure that they are adaptive to the WiFi user behavior. In order to achieve this goal, we characterize and model the user behavior across online time pattern and spatiotemporal mobility pattern. Then we propose BDAC, a behaviour-aware dynamic adaptive configuration, which is combined of two strategies: adaptive IP lease time configuration and dynamic IP pool configuration. The former is to set adaptive lease times across user roles and area types based on online time pattern to reclaim IP addresses in time and reduce the peak IP usage, while the latter dynamically migrates the IP addresses across VLANs based on spatio-temporal mobility correlation to save the IP addresses. Using the real network traces of a different week, we conduct experiments to evaluate the performance of BDAC. Results show that BDAC can save up to 60% of IP addresses and the actual IP utilization rises from 24% to 59%. Furthermore, BDAC maintains high IP utilization when the number of VLANs in a WLAN increases.