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A Differentiated Quality Adaptation Approach for Scalable Streaming Services

Providing scalable video streaming services for heterogeneous users in dynamic networked environments requires efficient and adaptive quality management mechanisms which deliver quality-customized services according to the client’s preferences and adapt the services to cope with various network conditions. In this paper, we address the issue of quality adaptation for providing personalized scalable media streaming services in dynamic network environments. We propose a differentiated adaptive quality optimization algorithm, called Scalable Video Coding Quality Adaptation algorithm (SVC-QA), which adapts streaming quality based on both system-level and client-level optimization to optimize streaming quality according to network bandwidth conditions, content characteristics, a user’s quality preferences, and buffering capacities of different client devices (e.g., mobile phones, PCs, HDTVs, etc.).

Comparative studies are conducted to compare our proposed algorithms with other adaptive methods. We show that two-level SVC quality adaptation method can achieve better SVC streaming quality with both high peak signal-to-noise ratio (PSNR) and low quality variance under dynamic resource constraints. Moreover, the proposed distributed method reduces the computational complexities at the server side substantially, making it practical and flexible for providing scalable streaming services.