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The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this article, we propose stall-aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall-aware pacing techniques extensively, including stall-aware pacing (SAP) and adaptive stall-aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.

The emerging popularity of live streaming services poses a great challenge for the rigid and static traditional Internet architecture. The rise in adaptation of Software Defined Networking (SDN) by Internet Service Providers (ISP) and Content Delivery Networks (CDN) presents an opportunity to dynamically adapt and respond in real-time to high definition (HD) mega events or dynamic short-lived broadcast events. In this paper, we present an SDN-based system design that utilizes a communication framework between ISPs and CDNs to interact and thus enable a reliable and resource efficient live streaming service. We build and deploy an optimization model that can maximize the video quality for users while minimizing the resource utilization for both ISPs and CDNs. The model considers device capabilities, network constraints and the subscription level of users with the ISP/CDN. Our system is a network-assisted, cross-layer, approach that implements multicast at the network layer and can dynamically adapt the video bitrates that are served to each client at the application layer. We build a prototype of our proposed design and evaluate real-world scenarios with up to 500 users streaming multiple videos at different bitrates. Results show that our approach can increase average user goodput by up to 70% while almost eliminating frame drops by handling network congestion.

Recent years have witnessed an explosion of multimedia traffic carried over the Internet. Video-on-demand and live streaming services are the most dominant services. To ensure growth, many streaming providers have invested considerable time and effort to keep pace with ever-increasing users' demand for better quality and stall abolition. HTTP adaptive streaming (HAS) algorithms are at the core of every major streaming provider service. Recent years have seen sustained development in HAS algorithms. Currently, to evaluate their proposed solutions, researchers need to create a framework and numerous state-of-the-art algorithms. Often, these frameworks lack flexibility and scalability, covering only a limited set of scenarios. To fill this gap, in this paper we propose DASHbed, a highly customizable real-time framework for testing HAS algorithms in a wireless environment. Due to its low memory requirement, DASHbed offers a means of running large-scale experiments with a hundred competing players. Finally, we supplement the proposed framework with a dataset consisting of results for five HAS algorithms tested in various evaluated scenarios. The dataset showcases the abilities of DASHbed and presents the adaptation metrics per segment in the generated content (such as switches, buffer-level, P. 1203.1 values, delivery rate, stall duration, etc.), which can be used as a baseline when researchers compare the output of their proposed algorithm against the state-of-the-art algorithms.

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.

In this article, we describe a neighbour disjoint multipath (NDM) scheme that is shown to be more resilient amidst node or link failures compared to the two well-known node disjoint and edge disjoint multipath techniques. A centralised NDM was first conceptualised in our initial published work utilising the spatial diversity among multiple paths to ensure robustness against localised poor channel quality or node failures. Here, we further introduce a distributed version of our NDM algorithm adapting to the low-power and lossy network (LLN) characteristics. We implement our distributed NDM algorithm in Contiki OS on top of LOADng—a lightweight On-demand Ad hoc Distance Vector Routing protocol. We compare this implementation's performance with a standard IPv6 Routing Protocol for Low power and Lossy Networks (RPL), and also with basic LOADng, running in the Cooja simulator. Standard performance metrics such as packet delivery ratio, end-to-end latency, overhead and average routing table size are identified for the comparison. The results and observations are provided considering a few different application traffic patterns, which serve to quantify the improvements in robustness arising from NDM. The results are confirmed by experiments using a public sensor network testbed with over 100 nodes.

The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric.

Opportunistic data collection in wireless sensor networks uses passing smartphones to collect data from sensor nodes, thus avoiding the cost of multiple static sink nodes. Based on the observed mobility patterns of smartphone users, sensor data should be preforwarded to the nodes that are visited more frequently with the aim of improving network throughput. In this article, we construct a formal network model and an associated theoretical optimization problem to maximize the throughput subject to energy constraints of sensor nodes. Since a centralized controller is not available in opportunistic data collection, data pre-forwarding (DPF) must operate as a distributed mechanism in which each node decides when and where to forward data based on local information. Hence, we develop a simple distributed DPF mechanism with two heuristic algorithms, implement this proposal in Contiki-OS, and evaluate it thoroughly. We demonstrate empirically, in simulations, that our approach is close to the optimal solution obtained by a centralized algorithm. We also demonstrate that this approach performs well in scenarios based on real mobility traces of smartphone users. Finally, we evaluate our proposal on a small laboratory testbed, demonstrating that the distributed DPF mechanism with heuristic algorithms performs as predicted by simulations, and thus that it is a viable technique for opportunistic data collection through smartphones.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

The design of an adaptive video client for mobile users is challenged by the frequent changes in operating conditions. Such conditions present a seemingly insurmountable challenge to adaptation algorithms, which may fail to find a balance between video rate, stalls, and rate-switching. In an effort to achieve the ideal balance, we design OSCAR, a novel adaptive streaming algorithm whose adaptation decisions are optimized to avoid stalls while maintaining high video quality. Our performance evaluation, using real video and channel traces from both 3G and 4G networks, shows that OSCAR achieves the highest percentage of stall-free sessions while maintaining a high quality video in comparison to the state-of-the-art algorithms.

Demanding new Internet applications have produced a radical and fundamental shift in the nature of entertainment.

Internet multimedia traffic is increasing as applications like streaming media and packet telephony grow in popularity. It is important to monitor the volume and characteristics of this traffic, particularly because its behavior in the face of network congestion differs from that of the currently dominant TCP traffic. To monitor traffic on a high-speed link for extended periods, it is not practical to blindly capture all packets that traverse the link. We present mmdump , a tool that parses messages from RTSP, H.323 and similar multimedia session control protocols to set up and tear down packet filters as needed to gather traces of multimedia sessions. Unlike tcpdump, dynamic packet filters are necessary because these protocols dynamically negotiate TCP and UDP port numbers to carry the media content. Our tool captures only packets of interest for optional storage and further analysis, thus greatly reducing resource requirements. This paper presents the design and implementation of mmdump and demonstrates its utility in monitoring live RTSP and H.323 traffic on a commercial IP network. The preliminary results obtained from these measurements are presented.