Recent studies have shown that utilizing a mobile sink (MS) to harvest and carry data from a wireless sensor network (WSN) can enhance network operations and increase the network lifetime. Since a significant portion of sensor nodes' energy is consumed for data transmission to an MS, the specific trajectory has a profound influence on the lifetime of a WSN. In this paper, we study the problem of controlling sink mobility in event-driven applications to achieve maximum network lifetime. In these applications, an MS with limited velocity has to harvest the captured data of events from a group of sensor nodes in a specific time as a reporting time slot. We show that this problem is NP-hard, and then, we propose a convex optimization model inspired by the support vector regression technique to determine an optimal trajectory of an MS without considering predefined structures such as a virtual grid or rendezvous points. The effectiveness of our approach is validated via the extensive number of simulation runs and comparison with other algorithms.

With the ever-increasing demands for high-definition and low-latency video streaming applications, network-assisted video streaming schemes have become a promising complementary solution in the HTTP Adaptive Streaming (HAS) context to improve users’ Quality of Experience (QoE) as well as network utilization. Edge computing is considered one of the leading networking paradigms for designing such systems by providing video processing and caching close to the end-users. Despite the wide usage of this technology, designing network-assisted HAS architectures that support low-latency and high-quality video streaming, including edge collaboration is still a challenge. To address these issues, this article leverages the Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing paradigms to propose A collaboRative edge-Assisted framewoRk for HTTP Adaptive video sTreaming (ARARAT). Aiming at minimizing HAS clients’ serving time and network cost, besides considering available resources and all possible serving actions, we design a multi-layer architecture and formulate the problem as a centralized optimization model executed by the SDN controller. However, to cope with the high time complexity of the centralized model, we introduce three heuristic approaches that produce near-optimal solutions through efficient collaboration between the SDN controller and edge servers. Finally, we implement the ARARAT framework, conduct our experiments on a large-scale cloud-based testbed including 250 HAS players, and compare its effectiveness with state-of-the-art systems within comprehensive scenarios. The experimental results illustrate that the proposed ARARAT methods (i) improve users’ QoE by at least 47%, (ii) decrease the streaming cost, including bandwidth and computational costs, by at least 47%, and (iii) enhance network utilization, by at least 48% compared to state-of-the-art approaches.

Live video streaming traffic and related applications have experienced significant growth in recent years. However, this has been accompanied by some challenging issues, especially in terms of resource utilization. Although IP multicasting can be recognized as an efficient mechanism to cope with these challenges, it suffers from many problems. Applying software-defined networking (SDN) and network function virtualization (NFV) technologies enable researchers to cope with IP multicasting issues in novel ways. In this article, by leveraging the SDN concept, we introduce OSCAR (Optimizing reSourCe utilizAtion in live video stReaming) as a new cost-aware video streaming approach to provide advanced video coding (AVC)-based live streaming services in the network. In this article, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder function (VTF). At the edge of the network, VRPs are responsible for collecting clients' requests and sending them to an SDN controller. Then, by executing a mixed-integer linear program (MILP), the SDN controller determines a group of optimal multicast trees for streaming the requested videos from an appropriate origin server to the VRPs. Moreover, to elevate the efficiency of resource allocation and meet the given end-to-end latency threshold, OSCAR delivers only the highest requested quality from the origin server to an optimal group of VTFs over a multicast tree. The selected VTFs then transcode the received video segments and transmit them to the requesting VRPs in a multicast fashion. To mitigate the time complexity of the proposed MILP model, we present a simple and efficient heuristic algorithm that determines a near-optimal solution in polynomial time. Using the MiniNet emulator, we evaluate the performance of OSCAR in various scenarios. The results show that OSCAR surpasses other SVC- and AVC-based multicast and unicast approaches in terms of cost and resource utilization.

Service Function Chain (SFC) provides an end-to-end service by processing traffic flow through a series of Virtual Network Functions (VNFs) in a specific order. Satisfying user’s demands (e.g, end-to-end delay) on one hand and minimizing the cost of SFC deployment in terms of energy and resource on the other hand, introduces VNFs placement as a crucial issue that is receiving significant attention by researchers. To address this problem and boost the performance of SFC, different techniques such as Network Function (NF) distribution, NF parallelism and optimal resource allocation have been utilized. Applying these mechanisms imposes other costs which must be taken into account by network providers. In this paper, we introduce CoDeC as a Cost-effective and Delay-aware resource allocation approach. By having user defined end-to-end threshold and using aforementioned mechanisms, CoDeC tries to place the requested VNFs with the minimum cost of deployment, distribution, parallelism and energy. Therefore, we formulate the addressed problem in form of Mixed Integer linear Programming (MILP) model. We then show that the problem is NP-complete and suffers from high time complexity in large-scale scenarios. Thus, a heuristic algorithm is introduced to determine a near-optimal solution in a reasonable amount of time. Our simulation results show that CoDeC achieves better performance in term of cost and acceptance rate compared to using each mechanism individually.

While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers locations. The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.