6 research outputs found
Real-Time Neural Video Recovery and Enhancement on Mobile Devices
As mobile devices become increasingly popular for video streaming, it's
crucial to optimize the streaming experience for these devices. Although deep
learning-based video enhancement techniques are gaining attention, most of them
cannot support real-time enhancement on mobile devices. Additionally, many of
these techniques are focused solely on super-resolution and cannot handle
partial or complete loss or corruption of video frames, which is common on the
Internet and wireless networks.
To overcome these challenges, we present a novel approach in this paper. Our
approach consists of (i) a novel video frame recovery scheme, (ii) a new
super-resolution algorithm, and (iii) a receiver enhancement-aware video bit
rate adaptation algorithm. We have implemented our approach on an iPhone 12,
and it can support 30 frames per second (FPS). We have evaluated our approach
in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows
that our approach enables real-time enhancement and results in a significant
increase in video QoE (Quality of Experience) of 24\% - 82\% in our video
streaming system
Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions
Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined
Energy-aware adaptive solutions for multimedia delivery to wireless devices
The functionality of smart mobile devices is improving rapidly but these devices are limited
in terms of practical use because of battery-life. This situation cannot be remedied by simply
installing batteries with higher capacities in the devices. There are strict limitations in the
design of a smartphone, in terms of physical space, that prohibit this “quick-fix” from being
possible. The solution instead lies with the creation of an intelligent, dynamic mechanism for
utilizing the hardware components on a device in an energy-efficient manner, while also
maintaining the Quality of Service (QoS) requirements of the applications running on the
device.
This thesis proposes the following Energy-aware Adaptive Solutions (EASE):
1. BaSe-AMy: the Battery and Stream-aware Adaptive Multimedia Delivery (BaSe-AMy)
algorithm assesses battery-life, network characteristics, video-stream properties and
device hardware information, in order to dynamically reduce the power consumption of
the device while streaming video. The algorithm computes the most efficient strategy for
altering the characteristics of the stream, the playback of the video, and the hardware
utilization of the device, dynamically, while meeting application’s QoS requirements.
2. PowerHop: an algorithm which assesses network conditions, device power consumption,
neighboring node devices and QoS requirements to decide whether to adapt the
transmission power or the number of hops that a device uses for communication.
PowerHop’s ability to dynamically reduce the transmission power of the device’s
Wireless Network Interface Card (WNIC) provides scope for reducing the power
consumption of the device. In this case shorter transmission distances with multiple hops
can be utilized to maintain network range.
3. A comprehensive survey of adaptive energy optimizations in multimedia-centric wireless
devices is also provided.
Additional contributions:
1. A custom video comparison tool was developed to facilitate objective assessment of
streamed videos.
2. A new solution for high-accuracy mobile power logging was designed and implemented
XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas
Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI
XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas
Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI