How will Deep Learning Change Internet Video Delivery?
Explore this paper's citation graph
- Type
- article
- Published
- 2017-11-30
- Cited by
- 54
- References
- 55
- OpenAlex
- https://openalex.org/W2769799194
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5917257
Keywords
Citation, Computer science, The Internet, World Wide Web, Multimedia
References
- The MPEG handbook : MPEG-1, MPEG-2, MPEG-4
- Video (language) modeling: a baseline for generative models of natural videos
- Learning to compare image patches via convolutional neural networks
- Learning Fine-Grained Image Similarity with Deep Ranking
- The Akamai network: a platform for high-performance internet applications
- Impact of marker density on the accuracy of association mapping
- Measurement Study of Netflix, Hulu, and a Tale of Three CDNs
- Learning visual similarity for product design with convolutional neural networks
- Practical, Real-time Centralized Control for CDN-based Live Video Delivery
- Learning Deep Architectures for AI
- Impact of frame rate and resolution on objective QoE metrics
- MPEG-4 and H.263 video traces for network performance evaluation
- Confused, timid, and unstable: picking a video streaming rate is hard
- Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing
- Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE
- An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
- DONAR: decentralized server selection for cloud services
- Optimizing cost and performance for content multihoming
- Deeply-Recursive Convolutional Network for Image Super-Resolution
- Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Cited by
- Leveraging interconnections for performance: the serving infrastructure of a large CDN
- Network Support for AR/VR and Immersive Video Application: A Survey
- Neural Adaptive Content-aware Internet Video Delivery
- Neural Networks Meet Physical Networks: Distributed Inference Between Edge Devices and the Cloud
- Dejavu: Enhancing Videoconferencing with Prior Knowledge
- Edge Assisted Real-time Object Detection for Mobile Augmented Reality
- Mobile Immersive Computing: Research Challenges and the Road Ahead
- RTSRGAN: Real-Time Super-Resolution Generative Adversarial Networks
- A GAN to Fight Video-related Traffic Flooding: Super-resolution
- Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning
- NEMO: enabling neural-enhanced video streaming on commodity mobile devices
- Towards Modality Transferable Visual Information Representation with Optimal Model Compression
- Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions
- Deep Neural Network–based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
- Cost-Optimized Video Transfer using Real-Time Super Resolution Convolutional Neural Networks
- Invertible Rescaling Network and Its Extensions
- AdaEnlight
- Adaptive Video Streaming in Multi-Tier Computing Networks: Joint Edge Transcoding and Client Enhancement
- Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation
- HyperRTV: Neural-Enhanced Adaptive Real-Time Video Streaming Based on Terminal-Edge Collaboration
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