QoE-driven live and on-demand LTE uplink video transmission

Abstract

Abstract-We consider the joint upstreaming of live and ondemand user-generated video content over LTE using a Qualityof-Experience driven approach. We contribute to the state-of-theart work on multimedia scheduling in three aspects: 1) we jointly optimize the transmission of live and time-shifted video under scarce uplink resources by transmitting a basic quality in realtime and uploading a refined quality for on-demand consumption.2) We propose a producer-consumer deadline-aware scheduling algorithm that incorporates both the physical state of the mobile producer (e.g., cache fullness) and the scheduled playout time at the end-user. 3) We show that the scheduling decisions in 1) and 2) can be determined locally for each mobile producer. We additionally present an analytical framework for de-centralized scalable video transmission and prove that there exists an optimal solution to our problem. Simulation results for LTE uplink further demonstrate the significance of our proposed optimization on the overall user experience.

Video-Quality-Driven Resource Allocation for Real-Time Surveillance Video Uplinking over OFDMA-based Wireless Networks

“…In (18), for each CS, the priority in (13) needs to be evaluated for all K channels and these K results in turn need to be sorted. Thus, (18) …”

“…Specifically, for bit-rate assignment in P1, VQD will be compared with a baseline utility-driven greedy algorithm over various scenarios [16]- [18].…”

“…These works focused on the downlink (DL) radio resource allocation problems. The greedy method in [16] has been extended to the scenario of UL video scheduling [17], [18]. However, these algorithms do not scale up well with increasing users due to the need of tracking resource and utility variations.…”

Cache-Enabled Uplink Transmission in Wireless Small Cell Networks

“…There has been a few works done to address the content uploading problems with cache considered [7]- [10]. In [7], the joint upstreaming of real-time and time-shifted on-demand videos under scarce uplink resources was optimized by considering cache at mobile terminals.…”

Self-tuning of scheduling parameters for balancing the quality of experience among services in LTE

“…In [22], a scheduling algorithm is proposed to deal with real-time and non-realtime traffic in a proportional fair manner. More recent works [23][24][25][26][27][28] propose QoE-aware schedulers whose aim is to optimize the overall QoE while ensuring a minimum QoE for all users. All of them decide the exact resources assigned to every single user in real time, which makes them suitable for minimum QoS/QoE assurance.…”