Papers related to video transmission/processing often appear in journals or conferences in the multimedia field, network field, and system field.
The most relevant top journals for video transmission/processing include TON, TMC, TIP, TCSVT, TMM, etc.
The most relevant top conference for video transmission/processing include SIGCOMM, MobiCom, INFOCOM, ACM MM, MMsys, etc.
Other relevant conference also include NSDI, OSDI, MobiSys, SOSP, ISCA, etc.
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SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity [NSDI 21]
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Learning in situ: a randomized experiment in video streaming [NSDI 20] [code]
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OnRL: Improving Mobile Video Telephony via Online Reinforcement Learning [Mobicom 20]
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GROOT: A Real-time Streaming System of High-Fidelity Volumetric Videos [Mobicom 20]
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ViVo: Visibility-Aware Mobile Volumetric Video Streaming [Mobicom20]
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Stick: A Harmonious Fusion of Buffer-based and Learning-based Approach for Adaptive Streaming [Infocom20]
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Interpreting Deep Learning-Based Networking Systems [Sigcomm 20] [Metis]
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Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning [MM 19] [code]
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Learning to Coordinate Video Codec with Transport Protocol for Mobile Video Telephony [Mobicom 19] [Concerto]
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PiTree: Practical Implementation of ABR Algorithms Using Decision Trees [MM 19] [code]
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Jigsaw: Robust Live 4K Video Streaming [Mobicom 19]
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Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE [Infocom 19] [DeepCast]
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QFlow: A Reinforcement Learning Approach to High QoE Video Streaming over Wireless Networks [Mobihoc 19]
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Edge Computing Assisted Adaptive Mobile Video Streaming [TMC 19]
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Oboe: Auto-tuning Video ABR Algorithms to Network Conditions [Sigcomm 18]
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HotDASH: Hotspot Aware Adaptive Video Streaming using Deep Reinforcement Learning [ICNP 18] [code]
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QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning [MM 18]
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Ensemble Adaptive Streaming – A New Paradigm to Generate Streaming Algorithms via Specializations [TMC 18]
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Neural Adaptive Video Streaming with Pensieve [Sigcomm 17] [code]
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CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction [Sigcomm 16]
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BOLA: Near-optimal bitrate adaptation for online videos [Infocom 16]
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mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming [TMM 16]
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A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP [Sigcomm 15] [MPC]
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A Buffer-Based Approach to Rate Adaptation: Evidence from a Large Video Streaming Service [Sigcomm 14] [Buffer-Based]
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A Control-Theoretic Approach to Rate Adaption for DASH Over Multiple Content Distribution Servers [TCSVT 14]
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Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE [CoNEXT 12]
| Year | Method | Detail |
|---|---|---|
| 21 | Fugu [NSDI 21] | DNN (bandwidth prediction)+DP (control) |
| 20 | OnRL [Mobicom 20] | Online RL |
| 20 | Stick [Infocom 20] | Buffer-based+Learning-based |
| 19 | Comyco [MM 19], Concerto [Mobicom 19] | Imitation Learning |
| 19 | PiTree [MM 19] | Explainable Learning |
| 18 | Oboe [Sigcomm 18] | Auto-tuning parameters |
| 17 | Pensieve [Sigcomm 17] Update [ICML 19] | Reinforcement Learning |
| 16 | CS2P [Sigcomm 16] | |
| 16 | BOLA [Infocom 16] | Buffer-Based+Lyapunov Optimization |
| 15 | MPC [Sigcomm 15] | MPC |
| 14 | Buffer-Based [Sigcomm 14] | Buffer-Based |
| 12 | Rate-Based [CoNEXT 12] | Rate-Based |
- Efficient Volumetric Video Streaming Through Super Resolution [HotMobile 21]
- SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices [IMWUT 21]
- Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning [Sigcomm 20] [LiveNas]
- NEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile Devices [Mobicom 20]
- Streaming 360-Degree Videos Using Super-Resolution [Infocom 20] [code]
- SR360: Boosting 360-Degree Video Streaming with Super-Resolution [Nossdav 20]
- Improving Quality of Experience by Adaptive Video Streaming with Super-Resolution [Infocom 20]
- Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices [TMC 20]
- MobiSR: Effcient OnDevice Super-Resolution through Heterogeneous Mobile Processors [Mobicom 19]
- Dejavu: Enhancing Videoconferencing with Prior Knowledge [HotMobile 19]
- Bridging the Edge-Cloud Barrier for Real-time Advanced Vision Analytics [HotCloud 19]
- Neural Adaptive Content-aware Internet Video Delivery [OSDI 18] [NAS] [code]
Some articles may be repeated.
- Look Ahead at the First-mile in Livecast with Crowdsourced Highlight Prediction [Infocom 20]
- Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning [Sigcomm 20] [LiveNas]
- MultiLive: Adaptive Bitrate Control for Low-delay Multi-party Interactive Live Streaming [Infocom 20]
- Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming [TMM 20]
- Optimizing Social Welfare of Live Video Streaming Services in Mobile Edge Computing [TMC 20]
- Intelligent Edge-Assisted Crowdcast with Deep Reinforcement Learning for Personalized QoE [Infocom 19] [DeepCast]
- Vantage: Optimizing video upload for time-shifted viewing of social live stream [Sigcomm 19]
- Edge-based Transcoding for Adaptive Live Video Streaming [HotEdge 19]
- QARC: Video Quality Aware Rate Control for Real-Time Video Streaming based on Deep Reinforcement Learning [MM 18]
- Characterizing User Behaviors in Mobile Personal Livecast: Towards an Edge Computing-assisted Paradigm [ToMM 18]
- Cloud-Assisted Crowdsourced Livecast [ToMM 17]
- Coping With Heterogeneous Video Contributors and Viewers in Crowdsourced Live Streaming: A Cloud-Based Approach [TMM 16]
- When Crowd Meets Big Video Data: Cloud-Edge Collaborative Transcoding for Personal Livecast [TNSE 15]
19 MM Grand Challenge:
- A Hybrid Control Scheme for Adaptive Live Streaming
- HD3: Distributed Dueling DQN with Discrete-Continuous Hybrid Action Spaces for Live Video Streaming
- Continuous Bitrate & Latency Control with Deep Reinforcement Learning for Live Video Streaming
- BitLat: Bitrate-adaptivity and Latency-awareness Algorithm for Live Video Streaming
- Latency Aware Adaptive Video Streaming using Ensemble Deep Reinforcement Learning
3-DOF
6-DOF, point cloud
- Efficient Volumetric Video Streaming Through Super Resolution [HotMobile 21]
- GROOT: A Real-time Streaming System of High-Fidelity Volumetric Videos [Mobicom 20]
- ViVo: Visibility-Aware Mobile Volumetric Video Streaming [Mobicom 20]
- Towards Viewport-dependent 6DoF 360 Video Tiled Streaming for Virtual Reality Systems [MM 20]
- User Centered Adaptive Streaming of Dynamic Point Clouds with Low Complexity Tiling [MM 20]
- Towards Viewport-dependent 6DoF 360 Video Tiled Streaming for Virtual Reality Systems [MM 20]
- A Pipeline for Multiparty Volumetric Video Conferencing: Transmission of Point Clouds over Low Latency DASH [MMsys 20]
- Cloud Rendering-based Volumetric Video Streaming System for Mixed Reality Services [MMsys 20]
- Low-latency Cloud-based Volumetric Video Streaming Using Head Motion Prediction [NOSSDAV 20]
- Emerging MPEG Standards for Point Cloud Compression [TCSVT 19]
- Rate-Utility Optimized Streaming of Volumetric Media for Augmented Reality [arxiv 18]
- Design, Implementation, and Evaluation of a Point Cloud Codec for Tele-Immersive Video [TCSVT 17]
Virtual reality papers research how to render with low latency in edge/cloud architecture. They often render small objects in mobile devices and render heavy background in the server.
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Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality [ASPLOS 21]
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Coterie: Exploiting Frame Similarity to Enable High-Quality Multiplayer VR on Commodity Mobile [ASPLOS 20]
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Firefly: Untethered Multi-user VR for Commodity Mobile Devices [ATC 20]
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MUVR: Supporting Multi-User Mobile Virtual Reality with Resource Constrained Edge Cloud [Egde Computing 18]
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Cutting the Cord: Designing a High-quality Untethered VR System with Low Latency Remote Rendering [MobiSys 18]
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Furion: Engineering High-Quality Immersive Virtual Reality on Today’s Mobile Devices [Mobicom 17]
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CloudVR: Cloud Accelerated Interactive Mobile Virtual Reality [MM 17]
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FlashBack: Immersive Virtual Reality on Mobile Devices via Rendering Memoization [MobiSys 16]
Papers about augmented reality deals with the inference of video analysis.
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Cuttlefish: Neural Configuration Adaptation for Video Analysis in Live Augmented Reality [TPDS 21]
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Edge Assisted Real-time Object Detection for Mobile Augmented Reality [Mobicom 19]
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Whiz: Data-Driven Analytics Execution [NSDI 21]
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Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo [NSDI 21]
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PECAM: Privacy-Enhanced Video Streaming and Analytics via Securely-Reversible Transformation [Mobicom 21]
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Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [arxiv 20]
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Decomposable Intelligence on Cloud-Edge IoT Framework for Live Video Analytics [IOTJ 20]
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Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics [Sigcom 20]
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Server-Driven Video Streaming for Deep Learning Inference [Sigcom 20]
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SPINN: Synergistic Progressive Inference of Neural Networks over Device and Clouds [Mobicom 20]
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Chameleon: Scalable adaptation of video analytics [Sigcomm 18]
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Noscope: Optimizing neural network queries over video at scale [VLDB 17]
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Live video analytics at scale with approximation and delay-tolerance [NSDI 17]
model training system:
- Optimus: an efficient dynamic resource scheduler for deep learning clusters [EuroSys 18]
- Learning without forgetting [ECCV 16]
- Scalable Bayesian Optimization Using Deep Neural Networks [ICML 15]
- Practical bayesian optimization of machine learning algorithms [NeuIPS 12]
Limited by the author's knowledge, some papers may be missed.