Video Transmission and Resource Allocation in Wireless Networks
Summary
Video traffic now dominates global mobile and fixed‐line networks, driving a pressing need to manage scarce spectrum and power resources while guaranteeing smooth, high‐quality playback. Wireless networks face challenges including fluctuating channel conditions, stringent latency requirements and uneven user demand. Modern solutions adopt cross‐layer designs that jointly consider application‐level rate adaptation, medium access control scheduling and physical‐layer parameters such as power, beamforming and modulation. Emerging paradigms such as network slicing and edge computing enrich resource allocation by localising content and control functions close to end users. Machine learning and optimisation techniques—ranging from deep reinforcement learning to graph neural networks—enable real‐time decisions that balance throughput, reliability and fairness. Beyond cellular systems, novel topologies including unmanned aerial vehicle relays and mm-wave small cells extend coverage and capacity, offering flexible resource allocation for scenarios as diverse as disaster relief, immersive remote education and autonomous vehicle teleoperation.
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Video Transmission and Resource Allocation in Wireless Networks publication trend
The graph below shows the total number of articles in video transmission and resource allocation in wireless networks across all publications each year (not limited to Nature Index journals).
Technical terms
Quality of Experience (QoE): A user‐centric metric that quantifies perceived video quality, encompassing factors such as resolution, stalling and smoothness of playback.
Device-to-Device (D2D) Communication: A paradigm enabling direct data exchange between user devices without traversing a central base station, easing network congestion.
Orthogonal Frequency-Division Multiple Access (OFDMA): A multiuser transmission technique that allocates distinct subsets of orthogonal subcarriers to different users to enhance spectral efficiency.
Graph Neural Network (GNN): A class of machine-learning models designed to process data structured as graphs, capturing spatial and relational dependencies among network nodes.
Unmanned Aerial Vehicle (UAV): A remotely piloted or autonomous aircraft used as a mobile relay to provide on-demand wireless coverage and dynamic resource allocation.
Peak Signal-to-Noise Ratio (PSNR): An objective video‐quality metric expressed in decibels that compares a reconstructed frame against its reference to gauge distortion.
References
- Power-Efficient UAV Positioning and Resource Allocation in UAV-Assisted Wireless Networks for Video Streaming with Fairness Consideration. Drones (2025).
- Cross Layer Power Allocation by Graph Neural Networks in Heterogeneous D2D Video Communications. IEEE Access (2025).
- QoE-Maximized Beam Assignment and Rate Control for Dynamic mm-Wave-Based Full-Duplex Small Cell Networks. IEEE Open Journal of the Communications Society (2023).
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