H.264 Coarse Grain Scalable (CGS) and Medium Grain Scalable (MGS) Encoded Video: A Trace Based Traffic and Quality Evaluation

Abstract

Abstract-The scalable video coding (SVC) extension of the H.264/AVC video coding standard provides two mechanisms, namely coarse grain scalability (CGS) and medium grain scalability (MGS), for quality scalable video encoding, which varies the fidelity (signal-to-noise ratio) of the encoded video stream. As H.264/AVC and its SVC extension are expected to become widely adopted for the network transport of video, it is important to thoroughly study their network traffic characteristics, including the bit rate variability. In this paper, we report on a large-scale study of the rate-distortion (RD) and rate variability-distortion (VD) characteristics of CGS and MGS. We found that CGS achieves low bit rate overheads in the 10-30% range compared to H.264 SVC single-layer encodings only for encodings with a total of up to three quality levels; more quality levels result in substantially higher overheads. The traffic variabilities of CGS are generally lower than for single-layer streams. We found that in the low to mid range of the MGS quality scalability, MGS can achieve the same or even slightly higher RD efficiency than corresponding single-layer encoding; toward the upper end of the MGS quality scalability range the RD efficiency drops off significantly. MGS layer extraction following the hierarchical B frame structure gives nearly as high RD performance as RD-optimized extraction. In the range of high RD efficiency, MGS streams have significantly higher traffic variabilities than single-layer streams at the frame time scale. At the group-of-pictures (GoP) time scale, MGS has similar or lower levels of traffic variability compared to single-layer streams. Generally, MGS layer extraction over the time horizon of individual GoPs gives significantly lower traffic variability than extraction over the time horizon of the full video sequence.

Video Transport Evaluation With H.264 Video Traces

“…As we observe in Fig. 5, and is confirmed in [80], this MGS-temporal layer strategy conducted for each individual GoP closely approximates the RD performance of the priority level extraction while avoiding its high computational cost. Conducting the MGS-temporal layer based extraction over the entire video sequence gives essentially identical results to the priority level extraction (which also operates over the entire video sequence).…”

“…For a given PSNR video quality, the H.264 CGS bitrate is 18-40 % higher than the corresponding H.264 single-layer bit rate. More extensive studies [80] confirmed these typical results and found that larger differences in the quantization parameters of the CGS layers (and correspondingly fewer CGS layers) lead to slightly smaller bit rate overheads of 10-30 % for encodings with two CGS enhancement layers. However, for smaller quantization parameter differences, there are typically substantially higher bit rate overheads on the order of 30-80 %.…”

Dynamic Resource Allocation and Layer Selection for Scalable Video Streaming in Femtocell Networks: A Twin-Time-Scale Approach

“…The FUEs randomly access one of the above three videos. Each GoP has a pattern of G16B15, i.e., IBBBBBBBBBBBBBBIBB, where hierarchical B frames were used, as recommended in [46]. The statistics concerning the video clips are described in Table II.…”

An Anatomy of SVC for Full HD Video Streaming

“…Li et al reported that according to the visual experience of users, SVC inter-layer prediction is more efficient for fast and complex sequences than for slow and simple scenarios [13]. Comparison studies on the performance of different quality scalability modes of SVC (namely CGS and MGS) over five long CIF videos revealed that MGS provides higher objective quality at the cost of higher rate variability [14], [15]. Recently, Slanina et al [16] studied the impact of the number of temporal and quality layers on the rate distortion performance of SVC, using two full HD video sequences encoded with constant frame rate.…”