Adaptive Video Streaming in Networked Environments

Adaptive video streaming has become the de facto approach for delivering high-quality video over heterogeneous networks, dynamically adjusting video bitrates to match fluctuating bandwidth and device capabilities. At its core, adaptive streaming segments media into short chunks and employs client-side algorithms to request the most appropriate representation based on real-time measurements of throughput, buffer occupancy and playback latency. This paradigm reduces rebuffering events and visual artefacts, enhancing end-user satisfaction. Network-level enhancements further refine delivery by leveraging edge computing, content delivery networks and multi-path transmissions to balance load, minimise latency and maximise resource utilisation. Machine learning techniques have increasingly informed bitrate adaptation and quality prediction, enabling proactive decisions that anticipate network changes and user behaviour. Concurrently, low-latency live streaming protocols and fairness metrics address the needs of large-scale live events and equitable resource allocation among simultaneous viewers. As video traffic continues to dominate Internet usage, the interplay between client intelligence, network orchestration and server provisioning remains central to achieving scalable, resilient and high-fidelity video services across wired, wireless and mobile infrastructures.

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Adaptive Video Streaming in Networked Environments publication trend

The graph below shows the total number of articles in adaptive video streaming in networked environments across all publications each year (not limited to Nature Index journals).

Technical terms

Adaptive Bitrate (ABR) streaming: A technique that dynamically adjusts video quality based on real-time network conditions and playback buffer status.

Quality of Experience (QoE): A user-centred metric assessing perceived video quality, including factors such as startup delay, resolution changes and rebuffering events.

Edge computing: The deployment of storage and computation resources at network peripheries to reduce latency and distribute workload closer to end users.

Low-latency live streaming: Methods and architectures designed to minimise end-to-end delay in live video delivery, typically targeting sub-five-second latencies.

References

1. QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing. IEEE Transactions on Broadcasting (2022).
2. $\mathsf{HxL3}$: Optimized Delivery Architecture for HTTP Low-Latency Live Streaming. IEEE Transactions on Multimedia (2022).
3. Balancing Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming. IEEE Access (2021).
4. Multipath-Based HTTP Adaptive Streaming Scheme for the 5G Network. IEEE Access (2020).
5. Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming. Sensors (2021).
6. A new QoE fairness index for QoE management. Quality and User Experience (2018).