## 2 Background and Related Work  
Video streaming on mobile devices consumes energy at all layers of the TCP/IP stack [25]. Solutions to this problem in the recent literature can be categorized into two types: the link-layer solutions that focus on managing the wireless network interface with energy-aware scheduling [26; 27; 28; 29], and application-layer solutions that alter the video decoding and processing functions. This manuscript focuses on a content-adaptive video-encoding solution for HEVC; thus the following section briefly elaborates on the state-of-the-art with respect to the application layer class of solutions. Certain application-layer approaches propose alterations to the decoder implementations (both software and hardware decoders) such as data and task-level parallelization techniques [30; 31]. The more advanced of variants of this concept, such as Green-MPEG, use metadata to specify the decoding-complexity requirements to the decoder [32; 33], which can then skip certain decoding operations in order to reduce the decoding energy consumption. The approach by [PERSON] et al. [34] is one such technique where two of the most complex decoding operations in the HEVC decoder (the in-loop filtering and the interpolation filters) are altered during the decoding process. However, such alterations in the interpolation filter at the decoder during motion compensation severely compromise the video quality. This is due to the use of a modified filter at the decoder on prediction unit (PU) residuals that are computed at an encoder which is unaware of the changes in the decoding process [3; 24]. The use of DVFS algorithms is seen as another common approach to reduce the energy consumption of a video decoder. In this case, the video quality and energy usage are balanced [11; 35; 36; 37], by controlling the idle-time of the video decoder in real-time by adjusting the CPU frequency and operational voltage [12]. However, this has been shown to have drawbacks such as frame drops and impact on the overall system performance which adversely affect the user's QoE, especially in the case of high frame rate, high quality video content [36]. The poor estimation of the complexity of subsequent frame/video segment is largely to blame in many cases, which leads to the sub-optimal selection of CPU frequencies and voltages. A recent Green-MPEG specification suggests the inclusion of codec-dynamic voltage frequency scaling (C-DVFS) metadata into the bit stream [13] to aid the frequency selection process in DVFS. However, estimating frame complexity in order to predict the operational CPU frequency still remains a challenging task. Such operations can greatly benefit from using an encoder (such as that proposed in this manuscript) which is capable of generating HEVC bit streams for given decoding-complexity and bit rate constraints. The encoder-side content adaptation offers another alternative to reduce the decoding energy consumption during video playback. For example, scalable video coding (SVC) using proxy servers, media transcoding solutions [27], and dynamic adaptive streaming technologies such as MPEG-DASH [20] facilitate dynamic video content adaptation in order to meet the constraints of video playback devices. However, in general, these and other similar solutions, such as device-oriented [23], battery-aware [19], adaptive multimedia delivery and rate adaptation [38] schemes, are limited to manipulating basic video coding parameters such as the quantization parameter (QP), spatial resolution, and frame rate to adapt the video content and achieve energy savings. Following a similar concept, decoder-friendly bit stream generation at the encoder has been attempted for H.264/AVC in [39; 40; 41]. For instance, the algorithm proposed in [39] constrains the encoder to select sub-pel motion vectors to control the decoding-complexity. A power consumption model for the deblocking filter is proposed in [40] to support the encoder in preparing decoder friendly H.264/AVC bit streams.