Resource Allocation with Incomplete Information for QoE-Driven Multimedia Communications

Abstract

Most existing Quality of Experience (QoE)-driven multimedia resource allocation methods assume that the QoE model of each user is known to the controller before the start of the multimedia playout. However, this assumption may be invalid in many practical scenarios. In this paper, we address the resource allocation problem with incomplete information where the realized mean opinion score (MOS) can only be observed over time, but the underlying QoE model and playout time are unknown. We consider two variants of this problem: 1) the form of the QoE model is known but the parameters are unknown; 2) both the form and the parameters of the QoE model are unknown. For both cases, we develop dynamic resource allocation schemes based on online test-optimization strategy. Simply speaking, one first spends appropriate time on testing the QoE model, then optimizes the sum of the MOS in the remaining playout time. The highlight of this paper lies in resolving the inherent tension between the test and optimization by jointly considering the uncertainties of QoE model and playout time. Furthermore, we derive tight bounds on the MOS loss incurred by the proposed schemes in comparison with the optimal scheme that knows the QoE model a priori and prove that the performance gap, as the playout time tends to infinity, asymptotically shrinks to zero.

Impact of video content and transmission impairments on quality of experience

“…Most of the recently proposed resource allocation schemes are designed based on QoE models [45]. The performances of these methods can be improved by considering the transmission impairments and video content related parameters, and their impact on QoE [26,39,44].…”

QoE-Driven D2D Media Services Distribution Scheme in Cellular Networks

“…In this paper, we define QoE functions to characterize the users' experience for different types of media service delivery. We consider the following usage media service type for D2D applications: (T1) Best Effort Service (BES): non-real-time service, such as file download or data transmission; (T2) Video Model: HDTV signal transmission, video on demand; (T3) Audio Model: digital radio broadcasting, lossless music service [6,7,14]. We take advantage of the different QoE function in the previous work to quantify the user's satisfaction [15], which are shown in the Table 1.…”

Lightweight QoE Driven and Invulnerability Guarantee Opportunistic Control Scheme for Wireless Sensor Networks

“…If the hop number matches the rule 1 NS F =NS 1 , go to step5. Otherwise, NS 1 would be improved according to formula (7). We will obtain the optimized NS 2 set.…”

“…Gómez G et al [6] proposed a novel architecture based on quality of experience (QoE) awareness for mobile operator networks. In article [7], the uncertainties of QoE model and playout time were considered jointly for resolving the inherent tension between the test and optimization. A mixed preemptive and non-preemptive resume priority (PRP/NPRP) M/G/1 queuing model was developed by Wu Y et.al [8] for modeling the spectrum usage behavior.…”