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- W2579586473 abstract "We employ a model-based engineering (MBE) of video rate adaptation functions on a bandwidth-constrained end-to-end network path: such as the Internet. The intrinsic complexity of video rate adaptive system arises from two estimation problems: first, the available bandwidth along the path, and second, the bandwidth demand of a video data flow exercised on the path. The well-known AIMD (additive increase multiplicative decrease) algorithm works reasonably well to get around these difficulties, albeit, with certain limitations that affect the QoE (quality of end-user user experience). The AIMD algorithm is unable to distinguish between the scenarios of a congestion in the network and a marginal over-rate allocation, as it searches for an optimal send rate of video. Consequently, AIMD does not have good handle on the convergence latency during a congestion relief, and the rate jitter incurred in the steady-state. In this paper, we provide extensions to AIMD in the form of dynamic plugin of the rate adjustment parameters, namely, how fast or slow the rate should be reduced or increased. The extended AIMD, armed with a system-level support mechanism for dynamc parameter adjustments, improves the overall QoE of end-users when reacting to congestion. The paper describes experimental results to demonstrate the viability of our extended AIMD." @default.
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- W2579586473 date "2013-10-01" @default.
- W2579586473 modified "2023-09-28" @default.
- W2579586473 title "QoE-cognizant video rate adaptation over bandwidth-limited network paths" @default.
- W2579586473 doi "https://doi.org/10.1109/lcn.2013.6761344" @default.
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