ABSTRACT
Video streaming currently dominates global Internet traffic. Live streaming broadcasts events in real-time, with very different characteristics compared to video-on-demand (VoD), being more sensitive to variations in delay, jitter, and packet loss. The use of adaptive streaming techniques over HTTP is massively deployed on the Internet, adapting the video quality to instantaneous condition of the network. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular adaptive streaming technology. In DASH, the client probes the network quality and adjusts the quality of requested video segment according to the bandwidth fluctuations. Therefore, DASH is an over-the-top application using unmanaged networks to distribute content in the best possible quality. In order to maintain a seamless playback, VoD applications commonly use a large reception buffer. However, in live streaming, the use of large buffers is not allowed because of the induced delay. Active Queue Management (AQM) arises as an alternative to control the congestion in router's queue, pressing the traffic sources to reduce their transmission rate when it detects incipient congestion. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming. Furthermore, we propose a new AQM algorithm to improve the user-perceived video quality. The results show that the proposed method achieves better performance than competing AQM algorithms and improves the video quality in terms of average peak signal-to-noise ratio while keeping the fairness among concurrent flows.
ABSTRACT
Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router's queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.
ABSTRACT
Low-cost video surveillance systems are attractive for Smart Home applications (especially in emerging economies). Those systems use the flexibility of the Internet of Things to operate the video camera only when an intrusion is detected. We are the only ones that focus on the design of protocols based on intelligent agents to communicate the video of an intrusion in real time to the guards by wireless or mobile networks. The goal is to communicate, in real time, the video to the guards who can be moving towards the smart home. However, this communication suffers from sporadic disruptions that difficults the control and drastically reduces user satisfaction and operativity of the system. In a novel way, we have designed a generic software architecture based on design patterns that can be adapted to any hardware in a simple way. The implanted hardware is of very low economic cost; the software frameworks are free. In the experimental tests we have shown that it is possible to communicate to the moving guard, intrusion notifications (by e-mail and by instant messaging), and the first video frames in less than 20 s. In addition, we automatically recovered the frames of video lost in the disruptions in a transparent way to the user, we supported vertical handover processes and we could save energy of the smartphone's battery. However, the most important thing was that the high satisfaction of the people who have used the system.