QOE-driven mobility management — Integrating the users' quality perception into network — Level decision making

QoE estimation and prediction using hidden Markov models in heterogeneous access networks

“…Piamrat et al [10] and Varela and Laulajainen [16], presented methods for QoE-aware network selection based on PSQA metric [12]. The PSQA metric mainly consider numerical values such as percentage of packet loss and mean loss burst size to output mean opinion score (MOS) based on Random Neural Networks (RNN).…”

“…III. SYSTEM MODEL Accurate QoE estimation and prediction requires correct path probing mechanism [8], [16]. This is vital for HAN which are prone to network level impairments such as network congestion and handoffs.…”

“…Firstly, there is a need to develop an efficient network path probing mechanism for all network interfaces of the mobile node (MNs) that require minimal bandwidth overhead. Secondly, methods for accurate QoE estimation and prediction are required that consider timevarying HAN conditions [8], [16]. The current approaches to handoffs, namely [4], do not explore the benefits of QoE estimation and prediction while making handoffs.…”

“…The current approaches to handoffs, namely [4], do not explore the benefits of QoE estimation and prediction while making handoffs. Further, approaches such as [16] and [10] simply assume the availability of underlying network path probing mechanism to facilitate handoffs. These assumptions are unrealistic and can severely hinder QoE provisioning in HAN [8], [16].…”

“…Further, approaches such as [16] and [10] simply assume the availability of underlying network path probing mechanism to facilitate handoffs. These assumptions are unrealistic and can severely hinder QoE provisioning in HAN [8], [16]. We assert that current approaches to QoE-aware handoffs lack efficient network path probing mechanism and methods for accurate QoE estimation and prediction in HANs.…”