A Test Bench for Distortion-Energy Optimization of a DSP-Based H.264/SVC Decoder

Pescador, F.; Juárez, E.; Samper, David; Sanz, C.; Raulet, Mickaël

doi:10.1109/dsd.2010.109

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, according to [8,15], the battery consumption in mobile video players shows some kind of dependence on the number of layers received. Therefore, the total energy consumption will depend on the number of layers that will be received so that total consumption along the whole reproduction time can be expressed as follows:…”

Section: Energy/qoe Single Optimizationmentioning

confidence: 99%

“…In Figure 8, the results obtained with the heuristics for both Situations 1 and 2 in the objective space are depicted. calculate FP(t 1 , t 2 , t 3 , t 4 , BAT FP ) {Obtain QoE for FP} (13) If minimize Energy then {minimize energy for a given QoE constraint} (14) ← minimum Acceptable QoE (15) ← find Plane( , QoE , BAT , BAT FP , QoE FP ) Note that, even for Situation 1 the heuristic provides values very close to the Pareto set. The reason is that 2L, 3L and the points in the 1L-4L path belong to the Pareto front.…”

Section: Maximizing User Qoe Constrained By Maximum Energy Consumptiomentioning

confidence: 99%

See 1 more Smart Citation

Dealing with Energy-QoE Trade-Offs in Mobile Video

Liberal

Taboada

Fajardo

2013

Journal of Computer Networks and Communications

View full text Add to dashboard Cite

Scalable video coding allows an efficient provision of video services at different quality levels with different energy demands. According to the specific type of service and network scenario, end users and/or operators may decide to choose among different energy versus quality combinations. In order to deal with the resulting trade-off, in this paper we analyze the number of video layers that are worth to be received taking into account the energy constraints. A single-objective optimization is proposed based on dynamically selecting the number of layers, which is able to minimize the energy consumption with the constraint of a minimal quality threshold to be reached. However, this approach cannot reflect the fact that the same increment of energy consumption may result in different increments of visual quality. Thus, a multiobjective optimization is proposed and a utility function is defined in order to weight the energy consumption and the visual quality criteria. Finally, since the optimization solving mechanism is computationally expensive to be implemented in mobile devices, a heuristic algorithm is proposed. This way, significant energy consumption reduction will be achieved while keeping reasonable quality levels.

show abstract

Section: Energy/qoe Single Optimizationmentioning

confidence: 99%

Section: Maximizing User Qoe Constrained By Maximum Energy Consumptiomentioning

confidence: 99%