Adaptive intra refresh for robust wireless multi-view video

This thesis was submitted for the award of PhD and was awarded by Brunel University LondonMobile wireless communication technology is a fast developing field and every day new mobile communication techniques and means are becoming available. In this thesis multi-view video (MVV) is also refers to as 3D video. Thus, the 3D video signals through wireless communication are shaping telecommunication industry and academia. However, wireless channels are prone to high level of bit and burst errors that largely deteriorate the quality of service (QoS). Noise along the wireless transmission path can introduce distortion or make a compressed bitstream lose vital information. The error caused by noise progressively spread to subsequent frames and among multiple views due to prediction. This error may compel the receiver to pause momentarily and wait for the subsequent INTRA picture to continue decoding. The pausing of video stream affects the user's Quality of Experience (QoE). Thus, an error resilience strategy is needed to protect the compressed bitstream against transmission errors. This thesis focuses on error resilience Adaptive Intra Refresh (AIR) technique. The AIR method is developed to make the compressed 3D video more robust to channel errors. The process involves periodic injection of Intra-coded macroblocks in a cyclic pattern using H.264/AVC standard. The algorithm takes into account individual features in each macroblock and the feedback information sent by the decoder about the channel condition in order to generate an MVV-AIR map. MVV-AIR map generation regulates the order of packets arrival and identifies the motion activities in each macroblock. Based on the level of motion activity contained in each macroblock, the MVV-AIR map classifies frames as high or low motion macroblocks. A proxy MVV-AIR transcoder is used to validate the efficiency of the generated MVV-AIR map. The MVV-AIR transcoding algorithm uses spatial and views downscaling scheme to convert from MVV to single view. Various experimental results indicate that the proposed error resilient MVV-AIR transcoder technique effectively improves the quality of reconstructed 3D video in wireless networks. A comparison of MVV-AIR transcoder algorithm with some traditional error resilience techniques demonstrates that MVV-AIR algorithm performs better in an error prone channel. Results of simulation revealed significant improvements in both objective and subjective qualities. No additional computational complexity emanates from the scheme while the QoS and QoE requirements are still fully met.Tertiary Institution Trust Fund (TETFund) of Nigeri

A Survey on Energy Consumption and Environmental Impact of Video Streaming

Climate change challenges require a notable decrease in worldwide greenhouse gas (GHG) emissions across technology sectors. Digital technologies, especially video streaming, accounting for most Internet traffic, make no exception. Video streaming demand increases with remote working, multimedia communication services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube, Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making energy consumption and environmental footprint critical. This survey contributes to a better understanding of sustainable and efficient video streaming technologies by providing insights into the state-of-the-art and potential future directions for researchers, developers, and engineers, service providers, hosting platforms, and consumers. We widen this survey's focus on content provisioning and content consumption based on the observation that continuously active network equipment underneath video streaming consumes substantial energy independent of the transmitted data type. We propose a taxonomy of factors that affect the energy consumption in video streaming, such as encoding schemes, resource requirements, storage, content retrieval, decoding, and display. We identify notable weaknesses in video streaming that require further research for improved energy efficiency: (1) fixed bitrate ladders in HTTP live streaming; (2) inefficient hardware utilization of existing video players; (3) lack of comprehensive open energy measurement dataset covering various device types and coding parameters for reproducible research

Prediction of Quality of Experience for Video Streaming Using Raw QoS Parameters

Along with the rapid growth in consumer adoption of modern portable devices, video streaming is expected to dominate a large share of the global Internet traffic in the near future. Today user experience is becoming a reliable indicator for video service providers and telecommunication operators to convey overall end-to-end system functioning. Towards this, there is a profound need for an efficient Quality of Experience (QoE) monitoring and prediction. QoE is a subjective metric, which deals with user perception and can vary due to the user expectation and context. However, available QoE measurement techniques that adopt a full reference method are impractical in real-time transmission since they require the original video sequence to be available at the receiver’s end. QoE prediction, however, requires a firm understanding of those Quality of Service (QoS) factors that are the most influential on QoE. The main aim of this thesis work is the development of novel and efficient models for video quality prediction in a non-intrusive way and to demonstrate their application in QoE-enabled optimisation schemes for video delivery. In this thesis, the correlation between QoS and QoE is utilized to objectively estimate the QoE. For this, both objective and subjective methods were used to create datasets that represent the correlation between QoS parameters and measured QoE. Firstly, the impact of selected QoS parameters from both encoding and network levels on video QoE is investigated. The obtained QoS/QoE correlation is backed by thorough statistical analysis. Secondly, the development of two novel hybrid non-reference models for predicting video quality using fuzzy logic inference systems (FIS) as a learning-based technique. Finally, attention was move onto demonstrating two applications of the developed FIS prediction model to show how QoE is used to optimise video delivery

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor

TCP-Based distributed offloading architecture for the future of untethered immersive experiences in wireless networks

IMX '22: ACM International Conference on Interactive Media Experiences, 22-24 June 2022, Aveiro, Portugal.Task offloading has become a key term in the field of immersive media technologies: it can enable lighter and cheaper devices while providing them higher remote computational capabilities. In this paper we present our TCP-based offloading architecture. The architecture, has been specifically designed for immersive media offloading tasks with a particular care in reducing any processing overhead which can degrade the network performance. We tested the architecture for different offloading scenarios and conditions on two different wireless networks: WiFi and 5G millimeter wave technologies. Besides, to test the network on alternative millimeter wave configurations, currently not available on the actual 5G millimeter rollouts, we used a 5G Radio Access Network (RAN) real-time emulator. This emulator was also used to test the offloading architecture for an simulated immersive user sharing network resources with other users. We provide insights of the importance of user prioritization techniques for successful immersive media offloading. The results show a great performance for the tested immersive media scenarios, highlighting the relevance of millimeter wave technology for the future of immersive media applications.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No. 813391