Thu. Sep 22nd, 2022

This project proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation results validate that the proposed policy indeed outperforms existing policies.

We consider a time-slotted wireless network consisting of a wireless access point (AP) and a group of N mobile clients denoted by S tot = f1; 2; :::;Ng. Each client is downloading an on-demand video which has been pre-stored by video service providers. The video content is partitioned into packets and streamed to clients via the AP and the wireless links. On the AP’s side, we assume that the AP always has packets at hand for transmission to each video client. In other words, the throughput for the AP to acquire video content from video providers is assumed to be much larger than that between the AP and the mobile clients. We also assume that there is no network coding mechanism involved in the system. Thus, in each time slot, the AP can transmit data to at most one client. In a wireless network, the quality of a wireless channel usually changes with time. 

In this section, we study general fading channels where R can consist of any number of different rates. Unlike the case of ON-OFF channels, the stability region cannot be determined by a simple set of conditions as those in Lemma
1. Instead, we impose the following conditions to simplify the analysis. Let R(t; S) := max frn(t) : n 2 Sg and R(t) be the shorthand for R(t; S tot). We assume that boundary of the stability region, as it is not possible to increase qn for any client n without making the system un stabilizable. Besides, corresponds to the complete resource pooling condition. We also note that these conditions reduce to  when R = f0; r_g.


ALGORITHM DETAILS:Algorithm for DPG Formation


 Operating System           :   Linux
 Simulation Tool               :    NS2
 Documentation               :    Ms-Office

 CPU type                                  :    Intel Pentium 4
 Clock speed                              :    3.0 GHz
 Ram size                                   :    512 MB
 Hard disk capacity                    :    80 GB
 Monitor type                             :    15 Inch color monitor
 Keyboard type                          :     Internet keyboard
 CD -drive type                          :     52xmax

Y. Gao, B. Zheng, G. Chen, and Q. Li, “Algorithms for constrained k-nearest neighbor queries over moving object trajectories, GeoInformatica, vol. 14, no. 2, pp. 241_276, 2010.
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