Interference Shaping for Improved Quality of Experience for Real-Time Video Streaming

Abstract

Abstract-The unpredictability of the wireless medium poses a major challenge to delivering a high quality of experience (QoE) for real-time video services. Bursty co-channel interference is a prominent cause of wireless throughput variability, which leads to video QoE degradation, even for a fixed average channel quality. In this paper, we propose and analyze a network-level resource management algorithm termed interference shaping to smooth out the throughput variations (and hence improve the QoE) of video users by decreasing the peak rate of co-channel best effort users. Wireless link capacity variations are mapped to the realtime video packet loss rate, and the interference shaping QoE gain for video users is quantified by benchmarking against a modified multi-scale structural similarity (H-MS-SSIM) index. H-MS-SSIM is an accurate perceptual video quality metric that incorporates the important hysteresis effect whereby the current QoE (which is subjective) may strongly depend on the recent past. The proposed technique increases mean QoE and reduces the QoE variability over time, with a net perceptual increase of about 2-3x in illustrative settings while incurring insignificant decrease in the QoE for co-channel best effort users. Interference shaping can be implemented in both unicast and multicast real-time video streaming with much higher potential gains for multicast.Index Terms-Quality of experience; resource allocation and interference management; cross-layer design; bursty interference, heterogeneous networks.

An Adaptive Cross-Layer Architecture to Optimize QoS Provisioning in MANET

“…In [30], the creators utilized a contention chart to show the impedance reliance between various video transmission joins. [31] Proposed a system level impedance forming approach for continuous video spilling. Impedance Alignment procedure was utilized to relieve obstruction for video application in subjective helpful system [6].…”

Bursty interference-oriented video quality assessment method

“…In [39], SSIM was extended to measure video distortion considering motion information and sequence characteristics. SSIM was used to assess real-time video streaming quality in the bursty interference environment in [29]. Based on SSIM, SS-SSIM, CW-SSIM and MS-SSIM [40], other evaluation algorithms also were proposed, promoting the development of objective assessment algorithm.…”

Fair resource allocation for OFDMA multi-cell networks

“…Fig 3 shows the layout of RSC with different cells marked by different colors. This layout is generated according to PPP suggested in [1], [2] and [3]. A square in the center area includes the cells, whose performance statistics are collected during the simulation.…”

“…Then optimization principles are derived from the formulation in section III-C. Based on these principles, a distributed power control and bandwidth allocation algorithm is designed in section IV. Finally, evaluation is done by simulating the algorithm in 2-dimensional USC and RSC, and results are explained in section V. For RSC, base stations are randomly located according to a Poisson point process (PPP) following the model for the node locations used in [1], [2] and [3]. We present our conclusions and suggestions for future work in section V.…”