A pricing mechanism for scalable video delivery
Multimedia Systems - Special issue on multimedia networking
Quality-adaptive media streaming by priority drop
NOSSDAV '03 Proceedings of the 13th international workshop on Network and operating systems support for digital audio and video
Utility maximization in peer-to-peer systems
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Dynamic adaptive streaming over HTTP --: standards and design principles
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Watching Video over the Web: Part 1: Streaming Protocols
IEEE Internet Computing
Priority-based Media Delivery using SVC with RTP and HTTP streaming
Multimedia Tools and Applications
Quality selection for Dynamic Adaptive Streaming over HTTP with Scalable Video Coding
Proceedings of the 3rd Multimedia Systems Conference
Subjective Video Quality Assessment of HTTP Adaptive Streaming Technologies
Bell Labs Technical Journal
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
Optimized Cross-Layer Design for Scalable Video Transmission Over the IEEE 802.11e Networks
IEEE Transactions on Circuits and Systems for Video Technology
QoE-Centric management of multimedia networks through cooperative control loops
AIMS'13 Proceedings of the 7th IFIP WG 6.6 international conference on Autonomous Infrastructure, Management, and Security: emerging management mechanisms for the future internet - Volume 7943
Hi-index | 0.00 |
HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, video content is split into segments and encoded into multiple qualities, such that the quality of a video can be dynamically adapted during the HTTP download process. This has given rise to intelligent video players that strive to maximize Quality of Experience (QoE) by adapting the displayed quality based on the user's available bandwidth and device characteristics. HAS-based techniques have been widely used in Over-the-Top (OTT) video services. Recently, academia and industry have started investigating the merits of HAS in managed IPTV scenarios. However, the adoption of HAS in a managed environment is complicated by the fact that the quality adaptation component is controlled solely by the end-user. This prevents the service provider from offering any type of QoE guarantees to its subscribers. Moreover, as every user independently makes decisions, this approach does not support coordinated management and global optimization. These shortcomings can be overcome by introducing additional intelligence into the provider's network, which allows overriding the client's decisions. In this paper we investigate how such intelligence can be introduced into a managed multimedia access network. More specifically, we present an in-network video rate adaptation algorithm that maximizes the provider's revenue and offered QoE. Furthermore, the synergy between our proposed solution and HAS-enabled video clients is evaluated.