Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects

Proxy Support for HTTP Adaptive Streaming

Not long ago streaming video over the Internet included only short clips of low quality video. Now the possibilities seem endless as professional productions are made available in high definition. This explosion of growth is the result of several factors, such as increasing network performance, advancements in video encoding technology, improvements to video streaming techniques, and a growing number of devices capable of handling video. However, despite the improvements to Internet video streaming this paradigm is still evolving. HTTP adaptive streaming involves encoding a video at multiple quality levels then dividing those quality levels into small chunks. The player can then determine which quality level to retrieve the next chunk from in order to optimize video playback when considering the underlying network conditions. This thesis first presents an experimental framework that allows for adaptive streaming players to be analyzed and evaluated. Evaluation is beneficial because there are several concerns with the adaptive video streaming ecosystem such as achieving a high video playback quality while also ensuring stable playback quality. The primary contribution of this thesis is the evaluation of prefetching by a proxy server as a means to improve streaming performance. This work considers an implementation of a proxy server that is functional with the extremely popular Netflix streaming service, and it is evaluated using two Netflix players. The results show its potential to improve video streaming performance in several scenarios. It effectively increases the buffer capacity of the player as chunks can be prefetched in advance of the player's request then stored on the proxy to be quickly delivered once requested. This allows for degradation in network conditions to be hidden from the player while the proxy serves prefetched data, preventing a reduction to the video quality as a result of an overreaction by the player. Further, the proxy can reduce the impact of the bottleneck in the network, achieving higher throughput by utilizing parallel connections to the server

An adaptive physiology-aware communication framework for distributed medical cyber physical systems

For emergency medical cyber-physical systems, enhancing the safety and effectiveness of patient care, especially in remote rural areas, is essential. While the doctor to patient ratio in the United States is 30 to 10,000 in large metropolitan areas, it is only 5 to 10,000 in most rural areas; and the highest death rates are often found in the most rural counties. Use of telecommunication technologies can enhance effectiveness and safety of emergency ambulance transport of patients from rural areas to a regional center hospital. It enables remote monitoring of patients by the physician experts at the tertiary center. There are critical times during transport when physician experts can provide vital assistance to the ambulance Emergency Medical Technicians (EMT) to associate best treatments. However, the communication along the roads in rural areas can range irregularly from 4G to low speed 2G links, including some parts of routes with cellular network communication breakage. This unreliable and limited communication bandwidth together with the produced mass of clinical data and the many information exchanges pose a major challenge in real-time supervision of patients. In this study, we define the notion of distributed emergency care, and propose a novel adaptive physiology-aware communication framework which is aware of the patient condition, the underlying network bandwidth, and the criticality of clinical data in the context of the specific diseases. Using the concept of distributed medical CPS models, we study the semantics relation of communication Quality of Service (QoS) with clinical messages, criticality of clinical data, and an ambulance's undertaken route all in a disease-aware manner. Our proposed communication framework is aimed to enhance remote monitoring of acute patients during ambulance transport from a rural hospital to a regional center hospital. We evaluate the components of our framework through various experimentation phases including simulation, instrumentation, real-world profiling, and validation

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems