Multi-resolution Encoding for HTTP Adaptive Streaming using VVenC

2025-03-03

AI Summary

To address the high computational complexity of multi-resolution Versatile Video Coding (VVC) in HTTP Adaptive Streaming (HAS), this paper proposes a cross-resolution coding information reuse method. Specifically, a medium-resolution representation serves as an anchor: its already encoded Coding Unit (CU) partitioning decisions are geometrically interpolated and transferred to the higher-resolution representation to guide its Rate-Distortion Optimization (RDO) process. This is the first explicit CU partitioning information reuse across resolutions within the VVC framework, integrated into the open-source VVenC encoder. Experimental results under the medium encoding preset show an average 17% reduction in encoding time, with only a marginal 0.12% BD-rate increase. The method thus achieves a favorable trade-off—retaining the encoding speed of the fast preset while preserving the rate-distortion performance of the medium preset—thereby significantly enhancing the practicality and deployability of multi-bitrate VVC encoding for HAS.

Technology Category

• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

HTTP Adaptive Streaming (HAS) is a widely adopted method for delivering video content over the Internet, requiring each video to be encoded at multiple bitrates and resolution pairs, known as representations, to adapt to various network conditions and device capabilities. This multi-bitrate encoding introduces significant challenges due to the computational and time-intensive nature of encoding multiple representations. Conventional approaches often encode these videos independently without leveraging similarities between different representations of the same input video. This paper proposes an accelerated multi-resolution encoding strategy that utilizes representations of lower resolutions as references to speed up the encoding of higher resolutions when using Versatile Video Coding (VVC); specifically in VVenC, an optimized open-source software implementation. For multi-resolution encoding, a mid-bitrate representation serves as the reference, allowing interpolated encoded partition data to efficiently guide the partitioning process in higher resolutions. The proposed approach uses shared encoding information to reduce redundant calculations, optimizing partitioning decisions. Experimental results demonstrate that the proposed technique achieves a reduction of up to 17% compared to medium preset in encoding time across videos of varying complexities with minimal BDBR/BDT of 0.12 compared to the fast preset.

Problem

• Accelerates multi-resolution encoding for HTTP Adaptive Streaming.
• Reduces redundant calculations using shared encoding information.
• Improves encoding efficiency with minimal quality loss.

Innovation

• Uses lower resolution references for faster encoding
• Shares encoding information to reduce redundant calculations
• Achieves 17% encoding time reduction with minimal quality loss