Joint video summarization and transmission adaptation for energy-efficient wireless video streaming

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A selective approach for energy-aware video content adaptation decision-taking engine in android based smartphone

Rapid advancement of technology and their increasing affordability have transformed mobile devices from a means of communication to tools for socialization, entertainment, work and learning. However, advancement of battery technology and capacity is slow compared to energy need. Viewing content with high quality of experience will consume high power. In limited available energy, normal content adaptation system will decrease the content quality, hence reducing quality of experience. However, there is a need for optimizing content quality of experience (QoE) in a limited available energy. With modification and improvement, content adaptation may solve this issue. The key objective of this research is to propose a framework for energy-aware video content adaptation system to enable video delivery over the Internet. To optimise the QoE while viewing streaming video on a limited available smartphone energy, an algorithm for energy-aware video content adaptation decision-taking engine named EnVADE is proposed. The EnVADE algorithm uses selective mechanism. Selective mechanism means the video segmented into scenes and adaptation process is done based on the selected scenes. Thus, QoE can be improved. To evaluate EnVADE algorithm in term of energy efficiency, an experimental evaluation has been done. Subjective evaluation by selected respondents are also has been made using Absolute Category Rating method as recommended by ITU to evaluate EnVADE algorithm in term of QoE. In both evaluation, comparison with other methods has been made. The results show that the proposed solution is able to increase the viewing time of about 14% compared to MPEG-DASH which is an official international standard and widely used streaming method. In term of QoE subjective test, EnVADE algorithm score surpasses the score of other video streaming method. Therefore, EnVADE framework and algorithm has proven its capability as an alternative technique to stream video content with higher QoE and lower energy consumption

Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.The EGIA project (KK-2022/00119The Consolidated Research Group MATHMODE (IT1456-22

Frame-based multiple-description video coding with extended orthogonal filter banks

We propose a frame-based multiple-description video coder. The analysis filter bank is the extension of an orthogonal filter bank which computes the spatial polyphase components of the original video frames. The output of the filter bank is a set of video sequences which can be compressed with a standard coder. The filter bank design is carried out by taking into account two important requirements for video coding, namely, the fact that the dual synthesis filter bank is FIR, and that loss recovery does not enhance the quantization error. We give explicit results about the required properties of the redundant channel filter and the reconstruction error bounds in case of packet errors. We show that the proposed scheme has good error robustness to losses and good performance, both in terms of objective and visual quality, when compared to single description and other multiple description video coders based on spatial subsampling. PSNR gains of 5 dB or more are typical for packet loss probability as low as 5%

The Effective Transmission and Processing of Mobile Multimedia

Ph.DDOCTOR OF PHILOSOPH