Authors :
Koffka Khan
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4a55nvxu
DOI :
https://doi.org/10.5281/zenodo.8143307
Abstract :
With the rapid advancement of drone
technology, the demand for high-quality and real-time
video streaming from drones has significantly increased.
However, existing classical computing techniques face
numerous challenges in meeting the requirements of
efficient video encoding, transmission, and decoding.
Quantum computing has emerged as a promising
approach to address these limitations by leveraging
quantum algorithms and principles to enhance various
aspects of video streaming systems. This paper presents a
taxonomy for the use of quantum computing in drone
video streaming technology. The taxonomy is designed to
categorize and classify the diverse applications of
quantum computing in the different stages of drone video
streaming, including video compression, encryption,
transmission, and decoding. The taxonomy framework
encompasses both theoretical and practical aspects,
considering the current state of quantum computing
technologies and their potential impact on drone video
streaming. The taxonomy is structured into several key
dimensions, including quantum algorithms, quantum-
enhanced data compression techniques, quantum
encryption methods, and quantum-assisted transmission
and decoding strategies. Each dimension is further
divided into subcategories that explore specific
approaches and methodologies relevant to quantum
computing in drone video streaming. Furthermore, the
paper highlights the benefits and challenges associated
with the adoption of quantum computing in this domain.
It discusses how quantum computing can improve video
compression efficiency, enhance data security through
quantum encryption algorithms, enable faster and more
reliable transmission using quantum-assisted techniques,
and facilitate real-time video decoding with the aid of
quantum algorithms. Through this taxonomy, researchers
and practitioners in the field of drone video streaming
can gain a comprehensive understanding of the potential
applications and implications of quantum computing. It
serves as a valuable reference for exploring innovative
solutions and design considerations to harness the power
of quantum computing in improving the performance and
capabilities of drone video streaming systems.
Keywords :
Quantum Computing, Drone Video Streaming, Taxonomy, Video Compression, Encryption, Transmission, Decoding.
With the rapid advancement of drone
technology, the demand for high-quality and real-time
video streaming from drones has significantly increased.
However, existing classical computing techniques face
numerous challenges in meeting the requirements of
efficient video encoding, transmission, and decoding.
Quantum computing has emerged as a promising
approach to address these limitations by leveraging
quantum algorithms and principles to enhance various
aspects of video streaming systems. This paper presents a
taxonomy for the use of quantum computing in drone
video streaming technology. The taxonomy is designed to
categorize and classify the diverse applications of
quantum computing in the different stages of drone video
streaming, including video compression, encryption,
transmission, and decoding. The taxonomy framework
encompasses both theoretical and practical aspects,
considering the current state of quantum computing
technologies and their potential impact on drone video
streaming. The taxonomy is structured into several key
dimensions, including quantum algorithms, quantum-
enhanced data compression techniques, quantum
encryption methods, and quantum-assisted transmission
and decoding strategies. Each dimension is further
divided into subcategories that explore specific
approaches and methodologies relevant to quantum
computing in drone video streaming. Furthermore, the
paper highlights the benefits and challenges associated
with the adoption of quantum computing in this domain.
It discusses how quantum computing can improve video
compression efficiency, enhance data security through
quantum encryption algorithms, enable faster and more
reliable transmission using quantum-assisted techniques,
and facilitate real-time video decoding with the aid of
quantum algorithms. Through this taxonomy, researchers
and practitioners in the field of drone video streaming
can gain a comprehensive understanding of the potential
applications and implications of quantum computing. It
serves as a valuable reference for exploring innovative
solutions and design considerations to harness the power
of quantum computing in improving the performance and
capabilities of drone video streaming systems.
Keywords :
Quantum Computing, Drone Video Streaming, Taxonomy, Video Compression, Encryption, Transmission, Decoding.
