The escalating demand for multimedia content requires efficient and scalable distribution solutions for adaptive streaming, particularly MPEG-DASH, in web environments. This paper presents SwarmLayer, a novel hybrid peer-to-peer (P2P) assisted streaming solution designed to enhance web content distribution. SwarmLayer integrates a traditional client-server model with a P2P paradigm, where active users concurrently serve as content distribution nodes by sharing acquired video segments. This approach aims to significantly reduce the load of the origin server, minimize redundant content transmission, and optimize network bandwidth utilization. Implemented as a transparent and non-intrusive intermediate layer between the media player and the content distribution system, SwarmLayer requires no modifications to existing application logic or infrastructure. Its design leverages standard web technologies, including WebRTC for peer connectivity, Service Workers for request interception, and Protocol Buffers for efficient data serialization, along with the MPEG-DASH standard. Experimental evaluations demonstrate substantial server offload, achieving up to a 96% reduction in server traffic, while maintaining seamless playback fluidity across diverse network conditions, including geographically distributed nodes and NAT environments.

This work addresses the integration of real-time transmission systems, including IP cameras and production systems (like OBS or vMix), that use protocols such as RTSP (Real Time Streaming Protocol) or SRT (Secure Reliable Transport), with content distribution technology based on LL-DASH (Low Latency DASH -Dynamic Adaptive Streaming over HTTP-), taking advantage of the fact that DASH offers significant well-known advantages for content distribution over the Internet and via CDNs (Content Delivery Networks). Considering the limitations of the LL-DASH standard regarding the adaptation to network conditions, this paper proposes a new solution called Server-Side Representation Switching (SSRS). SSRS uses an approach based on the server measuring the delay in the requests made by clients, whose variation may be due to a decrease in bandwidth, as occurs in Wi-Fi networks with a high number of clients. To evaluate the effectiveness of the proposed solution, a testbed has been developed that allows the performance evaluation of both the LL-DASH system and the solution based on server-side decision-making. In addition, the developed solution has been compared with known algorithms (L2A and LoL+) integrated into the Dash.js player. The results show that the Server-Side Representation Switching solution offers a good trade-off between the transmitted quality and the final delay measured at the client, compared to the other algorithms evaluated. Moreover, it holds the advantage of being straightforward to implement and does not require any modifications to the players used. • Networks • Network performance evaluation • Network performance analysis

Dynamic Streaming over HTTP (DASH) is the main standard used in online video streaming services, given that more than 1.2 billion pay subscribers around the world use this standard. This fact entails billions of streaming connections between video servers and client displays. These devices involved in the streaming connection use an Ethernet Interface Card that consumes energy. In order to reduce the energy consumption, IEEE proposed the 802.3az Energy Efficient Ethernet standard, with a mechanism to make the network card change to a low power consumption mode when it is not in transmission mode. This behavior will be beneficial for services where traffic is sent in bursts, for instance video packet bursts like in video streaming over Real Time Transport Protocol in IPTV or the widely used DASH standard. Therefore, in this study the Ethernet traffic pattern when transmitting online video content using DASH is characterized in order to analyze the efficiency of the IEEE 802.3az standard under this video streaming scenario, and to verify the convenience of activating this energy saving alternative at the network interface of billions of client devices. The experiments have been conducted using a test-bed consisting of a full DASH streaming architecture, comparing different video segment sizes and changing the available bandwidth during the experiments in different scenarios in order to analyze the effect of the DASH content segment size on the Ethernet traffic pattern to identify the trade-off between energy efficiency, the energy savings, and the impact on the performance of the dynamic adaptation on the video streaming and reproduction.

This work presents a wireless acoustic sensor network (WASN) that monitors urban environments by recognizing a given set of sound events or classes. The nodes of the WASN are Raspberry Pi devices that not only record the ambient sound, but also detect and recognize different sound events. All the signal processing tasks, from the recording to the classification carried out by a convolutional neural network (CNN), are run on Raspberry Pi devices. Due to the low cost of the proposed acoustic nodes, the system exhibits a very high potential scalability. Regarding the underlying WASN, it has been designed according to the open standard FIWARE, thus the whole system can be deployed without the need of proprietary software. Regarding the performance of the sound classifier, the proposed WASN achieves similar accuracy compared to other WASNs that make use of cloud computing. However, the proposed WASN significantly minimizes the network traffic since it does not exchange audio signals, but only contextual information in form of labels. On the other hand, most of the time the class reported by the WASN nodes is the “background” soundscape, which usually contains no event of interest. This is the case when monitoring the soundscape of big avenues, where four events have been identified: “traffic”, “siren”, “horn” and “noisy vehicles”, being the “traffic” class associated to the background soundscape. In this paper, the use of a simple pre-detection stage prior to the CNN classification is proposed, with the aim of saving computation and power consumption at the nodes. The pre-detection stage is able to differentiate the other three relevant sounds from the “traffic” and activates the classifier only when some of these three events is likely occurring. The proposed pre-detection stage has been validated through data recorded in the city of Valencia (Spain), achieving a reduction of the Raspberry Pi CPU’s usage by a factor of six.

Abstract Video streaming services have restrictive delay and bandwidth constraints. Ad hoc networks represent a hostile environment for this kind of real‐time data transmission. Emerging mesh networks, where a backbone provides more topological stability, do not even assure a high quality of experience. In such scenario, mobility of terminal nodes causes link breakages until a new route is calculated. In the meanwhile, lost packets cause annoying video interruptions to the receiver. This paper proposes a new mechanism of recovering lost packets by means of caching overheard packets in neighbor nodes and retransmit them to destination. Moreover, an optimization is shown, which involves a video‐aware cache in order to recover full frames and prioritize more significant frames. Results show the improvement in reception, increasing the throughput as well as video quality, whereas larger video interruptions are considerably reduced. Copyright © 2014 John Wiley & Sons, Ltd.