Open RAN is an emerging paradigm in mobile networks where the Radio Access Network (RAN) functions are disaggregated and virtualized on commodity servers. Despite the importance of Open RAN research, existing platforms often lack the fidelity and stability required to address a wide range of research problems. In response to this limitation, we have developed an enterprise-scale Open RAN testbed aimed at conducting state-of-the-art research in key areas that have received limited attention due to the lack of suitable platforms. In this poster, we provide an overview of the testbed we have created and examples of the research it has enabled, with the hope of catalyzing future open RAN research and innovation.

The papers in this special issue focus on trustworthiness in multimedia communications. Recently, social multimedia content is being delivered to users with a high quality of experience (QoE) with the advance of multimedia technologies and social networks. However, as a huge amount of social users have various demands to exchange and share multimedia content with each other, it becomes a new challenge for the current social multimedia analytics and delivery to deal with the various attacks perpetrated by malicious users or through spam contents. Therefore, the trust and risk management for social multimedia content based on the social tie of users become of prime importance to face the unpredicted threats and subsequent damage. This Special Section aims to provide a premier forum for researchers working on the trust-based social multimedia analytics and delivery. It also provides the opportunity for both academic and industrial researchers to discuss recent results and provide solutions to the above-mentioned challenges.

The papers in this special issue focus on cloud computing-based video processing and content sharing. With the rapid growth of IPTV and mobile video applications and driven by urgent demands from industry and users, video processing and content sharing technologies have received significant research attention in recent years. Cloud-based video processing and content sharing networks are promising technologies to orchestrate large-scale and efficient video distribution between mobile clients and multimedia cloud systems. The objective of this special issue is to identify and promote advancements in media cloud-based video processing and content sharing technologies to advance current and enable future anywhere and anytime video processing and streaming applications.

Real-time communication (RTC) applications such as VoIP, video conferencing, and online gaming are flourishing. To adapt and deliver good performance, these applications require accurate estimations of short-term network performance metrics, e.g., loss rate, one-way delay, and throughput. However, the wide variation in mobile cellular network performance makes running RTC applications on these networks problematic. To address this issue, various performance adaptation techniques have been proposed, but one common problem of such techniques is that they only adjust application behavior reactively after performance degradation is visible. Thus, proactive adaptation based on accurate short-term, fine-grained network performance prediction can be a preferred alternative that benefits RTC applications. In this study, we show that forecasting the short-term performance in cellular networks is possible in part due to the channel estimation scheme on the device and the radio resource scheduling algorithm at the base station. We develop a system interface called PROTEUS, which passively collects current network performance, such as throughput, loss, and one-way delay, and then uses regression trees to forecast future network performance. PROTEUS successfully predicts the occurrence of packet loss within a 0.5s time window for 98% of the time windows and the occurrence of long one-way delay for 97% of the time windows. We also demonstrate how PROTEUS can be integrated with RTC applications to significantly improve the perceptual quality. In particular, we increase the peak signal-to-noise ratio of a video conferencing application by up to 15dB and reduce the perceptual delay in a gaming application by up to 4s.

Mobile broadband networks using 3G and 4G technologies (such as EV-DO, HSPA, WiMAX, LTE) are rapidly becoming one of the prominent means to access the Internet. Multimedia consumption - requiring low delay, high bandwidth, or a combination of both - is projected to become a large portion of bandwidth utilization in mobile broadband networks. In this paper, we study the fundamental problem of how packet loss and delay vary as a function of the transmission rate over these networks. With extensive real-world measurement studies, we analyze the performance of a number of rate control algorithms commonly used in media transmission. We show that the variable nature of congestion signals (loss and delay) on these networks leads to an ultimate failure of existing rate control strategies to deliver adequate performance for multimedia applications. In addition, we show how a rate control algorithm derived from the utility maximization framework - which uses queuing delay as the primary congestion signal - can be modified to solve the challenging issues we have observed. By using a variable threshold to define when the network is congested, our proposed solution is able to achieve a significant improvement over algorithms that use fixed definitions of congestion.

The convergence of games and online social platforms is an exploding phenomena. The continued success of social games hinges critically on the ability to deliver smooth and highly-interactive experiences to end-users. However, it is extremely challenging to satisfy the stringent performance requirements of online social games.

With increasing computation power, network bandwidth, and improvements in display and capture technologies, fully immersive conferencing and tele-immersion is becoming ever closer to reality. Outside of video, one of the key components needed is high quality spatialized audio. This paper presents an implementation of a relatively low complexity, simple solution which allows realistic audio spatialization of arbitrary positions in a 3D video conference. When combined with pose tracking, it also allows the audio to change relative to which position on the screen the viewer is looking at.

Audio spatialization is becoming an important part of creating realistic experiences needed for immersive video conferencing and gaming. Using a combined head and room impulse response (CHRIR) has been recently proposed as an alternative to using separate head related transfer functions (HRTF) and room impulse responses (RIR). Accurate measurements of the CHRIR at various source and listener locations and orientations are needed to perform good quality audio spatialization. However, it is infeasible to accurately measure or model the CHRIR for all possible locations and orientations. Therefore, low-complexity and accurate interpolation techniques are needed to perform audio spatialization in real-time. In this paper, we present a frequency domain interpolation technique which naturally interpolates the interaural level difference (ILD) and interaural time difference (ITD) for each frequency component in the spectrum. The proposed technique allows for an accurate and low-complexity interpolation of the CHRIR as well as allowing for a low-complexity audio spatialization technique which can be used for both headphones as well as loudspeakers.

Depth cameras are gaining interest rapidly in the market as depth plus RGB is being used for a variety of applications ranging from foreground/background segmentation, face tracking, activity detection, and free viewpoint video rendering. In this paper, we present a low-complexity, near-lossless codec for coding depth maps. This coding requires no buffering of video frames, is table-less, can encode or decode a frame in close to 5ms with little code optimization, and provides between 7:1 to 16:1 compression ratio for near-lossless coding of 16-bit depth maps generated by the Kinect camera.

As cloud computing is taking off, the presence of high-performance interactive Internet applications is exploding. By nature, these applications require responsive client-server data exchange and lossless, in-order delivery. Previous work has shown that by using forward error correction (FEC), it is possible to reduce the data streaming latency caused by retransmissions of lost packets. However, the prior schemes only send FEC packets when there are no original packets pending transmission. In this paper, we further expand the hybrid FEC-ARQ protocol and show that sometimes, the transmission latency can be further reduced by preempting original data packets with FEC packets. We have formulated the decision of whether to send new original data packets, FEC packets, or resend original data packets as a transmission policy. An optimal transmission policy is selected to minimize the delay experienced by the application subject to a constraint on the amount of overhead. By using this optimal policy, we significantly improve the delay performance over straightforward FEC schemes while controlling the amount of overhead due to FEC.

There has been a dramatic increase in interactive cloud based software applications. Compared to classical real-time media applications (voice over IP (VoIP)/conferencing) and non real-time file delivery, these interactive software applications have unique characteristics: 1) they are delay sensitive yet demand in order and reliable data delivery, and 2) the traffic is usually bursty. Traditional window based congestion control does not work well for interactive applications because the bursty arrival of data leads to bursty network traffic, which causes additional queuing delay and packet loss in the network which reduces its delay performance. In this paper, we propose a new hybrid window plus rate based congestion control technique. This algorithm improves the delay performance of interactive applications by preventing congestion induced loss and minimizing queuing delay while still fully utilizing network capacity and maintaining fairness across multiple flows.

There is an ongoing explosion of interactive Internet applications. By nature, these applications require responsive client-server data exchange and lossless, in-order delivery. In previous work, we have shown that a hybrid FEC-ARQ protocol which combines sending original data packets with forward error correction (FEC) packets is effective in reducing the latency caused by retransmissions of lost packets. However, the prior scheme only sends FEC packets when there are no original packets pending transmission. In this paper, we further expand the investigation of the hybrid FEC-ARQ protocol and show that sometimes, the transmission latency can be further reduced by preempting original data packets with FEC packets. We have formulated the decision of whether to send new original data packets, FEC packets, or resend original data packets as a transmission policy. An optimal transmission policy is selected to minimize the delay experienced by the application subject to a constraint on the amount of waste. By using this optimal policy, we significantly improve the delay performance over straightforward FEC schemes while controlling the amount of waste due to FEC.

Recently, there has been a dramatic increase in interactive cloud based software applications (e.g. working on remote machines, online games, interactive websites such as financial, web search) and other soft real-time applications (traffic within data center). Compared to classical real-time media applications (VoIP/conferencing) and non real-time file delivery, these interactive software applications have unique characteristics as they are delay sensitive yet demand in order and reliable data delivery. Therefore existing protocols for delivery of lossless data (such as TCP) and other delivery protocols using UDP do not work well. In this demo, we show substantially improved performance for such traffic by using a transport protocol built on top of UDP which uses intelligent adaptive forward error correction (FEC) and improved congestion control (rate control). The transport protocol is made lossless by using a hybrid FEC/ARQ strategy. The congestion control technique improves the delay performance by preventing congestion induced loss and minimizing queuing delay while still fully utilizing network capacity and maintaining fairness across flows. In this demo, we present RAPID (a ReliAble transport Protocol for Improving end-to-end Delay) and show its effectiveness in improving the performance of interactive client-server applications.

Audio coding at low bitrates suffers from artifacts due to spectrum truncation. Typical audio codecs code multi-channel sources using transforms across the channels to remove redundancy such as middle (mid) - side (M/S) coding. At low bitrates, the spectrum of the coded channels is truncated and the spectrum of the channels with lower energy, such as the side channel, is truncated severely, sometimes entirely. This results in a muffled sound due to truncation of all coded channels beyond a certain frequency. It also results in a loss of spatial image even at low frequencies due to severe truncation of the side channel. Previously we have developed a low bitrate coding method to combat the loss of higher frequencies caused by spectrum truncation. In this paper, we present a novel low bitrate audio coding scheme to mitigate the loss of spatial image. Listening tests show that the combination of the two low bitrate coding methods results in a audio codec that can get good quality even at bitrates as low as 32kbps for stereo content with low decoder complexity.

Interactive Internet applications that rely on sequential streams for lossless data exchange often use retransmission protocols (e.g. TCP) for reliability and the guarantee of sequential data ordering. More so than for bulk file transfer or media delivery, lossless sequential streaming poses an even greater challenge for the common problem cases of retransmission protocols, such as lossy links or long network paths, manifesting as significant latency in the interactive user experience. We propose a hybrid FEC-ARQ protocol built on a packet streaming code that reduces to a simple strategy over sending or resending original data packets or check packets combining undecoded packets, based on actual network conditions. Experimental results show that our proposed protocol can significantly improve the total delay over retransmission and other schemes that use FEC, under a range of bandwidth and loss scenarios.

Media streaming over unreliable networks such as the Internet is growing in popularity, but presents unique challenges when trying to get the user experience to be on par with classical mediums such as cable television. These networks have variable network conditions which not only vary between a set of points in the network, but also change over time. In this paper, we present a rate-distortion (R-D) optimized algorithm for adapting the bitrate of streaming media from a chunked encoding, where each chunk is available at multiple bitrates (or quality levels) or is scalable coded. The optimization takes into account the desired startup latency, the desired client buffer size, the current client buffer size, and the estimated network bandwidth. The problem is formulated as a distortion minimization problem subject to multiple rate constraints. For video content, the solution gives a gain of up to 3-4 dB in PSNR in difficult portions of the video when compared to commonly used adaptation techniques and can achieve an arbitrarily small desired startup latency.

The use of multiple entropy models for Huffman or arithmetic coding is widely used to improve the compression efficiency of many algorithms when the source probability distribution varies. However, the use of multiple entropy models increases the memory requirements of both the encoder and decoder significantly. In this paper, we present an algorithm which maintains almost all of the compression gains of multiple entropy models for only a very small increase in memory over one which uses a single entropy model. This can be used for any entropy coding scheme such as Huffman or arithmetic coding. This is accomplished by employing multiple entropy models only for the most probable symbols and using fewer entropy models for the less probable symbols. We show that this algorithm reduces the audio coding bitrate by 5%–8% over an existing algorithm which uses the same amount of table memory by allowing effective switching of the entropy model being used as source statistics change over an audio transform block.

Audio coding at low bitrates typically suffers from artifacts caused by bandwidth truncation. In this paper we present a novel scheme to code audio signals at low bitrates which uses a traditional scalar quantization followed by entropy coding to code some portions of the spectrum (typically the lower portion). The other portions (typically the higher portions) of the spectrum are coded at a low bitrate using an adaptive gain shape vector quantizer where the codebook for vector quantization is formed by unmodified or modified versions of the portions of the spectrum which have already been coded. Fixed pre-trained codebooks are also available for use in certain cases. The use of such a scheme results in an audio codec which has been shown to be among the best audio codecs available at low bitrates. In addition, the decoder complexity of this audio codec is significantly lower than any other codec of equal quality at low bitrates.

We examine 3-D wavelet coding of video with arbitrary regions of support (AROS). A critically sampled wavelet transform is applied to the AROS and a modified 3-D set partitioning in hierarchical trees (SPIHT) algorithm is used to quantize and code the wavelet coefficients in the AROS only. Experiments show that, for typical MPEG-4 pre-segmented sequences, our proposed method can achieve a gain of up to 5.6 dB in average PSNR at the same rate over 3-D SPIHT coding of regular volumes that embed the AROS of the given video sequences.

We consider the problem of error control for receiver-driven layered multicast of audio and video over the Internet. The sender injects into the network multiple source layers and multiple channel coding (parity) layers, some of which are delayed relative to the source, Each receiver subscribes to the number of source layers and the number of parity layers that optimizes the receiver's quality for its available bandwidth and packet loss probability. We augment this layered FEC system with layered pseudo-ARQ. Although feedback is normally problematic in broadcast situations, ARQ can be simulated by having the receivers subscribe and unsubscribe to the delayed parity layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender and is scalable to an unlimited number of receivers, We show gains of 4-18 dB on channels with 20% loss over systems without error control and additional gains of 1-13 dB when FEC is augmented by pseudo-ARQ in a hybrid system, Optimal error control in the hybrid system is achieved by an optimal policy for a Markov decision process.

We consider the problem of joint source/channel coding of real-time sources, such as audio and video, for the purpose of multicasting over the Internet. The sender injects into the network multiple source layers and multiple channel (parity) layers, some of which are delayed relative to the source. Each receiver subscribes to the number of source layers and the number of channel layers that optimizes the source-channel rate allocation for that receiver's available bandwidth and packet loss probability. We augment this layered FEC system with layered ARQ. Although feedback is normally problematic in broadcast situations, ARQ is simulated by having the receivers subscribe and unsubscribe to the delayed channel coding layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender, and is scalable to an unlimited number of receivers. We show gains of up to 18 dB on channels with 20% loss over systems without error control, and additional gains of up to 13 dB when FEC is augmented by pseudo-ARQ in a hybrid system. The hybrid system is controlled by an optimal policy for a Markov decision process.

This paper presents a POCS-based algorithm for consistent reconstruction of a signal x/spl isin/R/sup K/ from any subset of quantized coefficients y/spl epsiv/R/sup N/ in an N/spl times/K overcomplete frame expansion y=Fx, N=2K. By choosing the frame operator F to be the concatenation of two K/spl times/K invertible transforms, the projections may be computed in R/sup K/ using only the transforms and their inverses, rather than in the larger space R/sup N/ using the pseudo-inverse as proposed in earlier work. This enables practical reconstructions from overcomplete frame expansions based on wavelet, subband, or lapped transforms of an entire image, which has heretofore not been possible.

Three-dimensional (2D+T) wavelet coding of video using SPIHT has been shown to outperform standard predictive video coders on complex high-motion sequences, and is competitive with standard predictive video coders on simple low-motion sequences. However, on a number of typical moderate-motion sequences characterized by largely rigid motions, 3D SPIHT performs several dB worse than motion-compensated predictive coders, because it is does not take advantage of the real physical motion underlying the scene. We introduce global motion compensation for 3D subband video coders, and find 0.5 to 2 dB gain on sequences with dominant background motion. Our approach is a hybrid of video coding based on sprites, or mosaics, and subband coding.

This paper presents an algorithm for image compression using finite state hierarchical table-lookup vector quantization. Finite state vector quantizers are vector quantizers with memory. Finite state vector quantization (FSVQ) takes advantage of the correlation between adjacent blocks of pixels in an image and also helps in overcoming the complexity problem of block memoryless VQ for large block sizes by using smaller block sizes for similar performance. FSVQ algorithms typically try to preserve edge and gray scale gradient continuity across block boundaries in images in order to reduce blockiness. Our algorithm combines FSVQ with hierarchical table-lookup vector quantization. Thus the full-search encoder in an FSVQ is replaced by a table-lookup encoder. In these table lookup encoders, input vectors to the encoder are used directly as addresses in code tables to choose the code-words. In order to preserve manageable table sizes for large dimension VQs, we use hierarchical structures to quantize the vector successively in stages. Since both the encoder and decoder are implemented by table lookups, there are no arithmetic computations required in the final system implementation. To further improve the subjective quality of compressed images we use block transform based finite-state table-lookup vector quantizers with subjective distortion measures. There is no need to perform the forward or reverse transforms as they are implemented in the tables.

We present an algorithm for image compression which involves adaptively segmenting a block of residuals resulting from prediction, while encoding them using hierarchical table lookup vector quantization. An optimum decomposition of the block allows an image to be adaptively quantized depending on the statistics of the residual block being encoded. This is useful since most images are nonstationary and have some regions with high detail and some with little. With an optimum decomposition, we can adaptively allocate bits by varying the block size to be quantized. Predictive vector quantization (PVQ) allows us to take advantage of the correlation between adjacent blocks of pixels being encoded by providing memory. The quadtree structure is used to represent the segmentation information and is sent as side information. To reduce the encoding complexity, we use hierarchical table lookups so no arithmetic computations have to be performed to find the minimum distortion codeword. To further improve the performance, we use a variable rate code to decrease the rate. Also, to improve the subjective quality of the image, we use subjective distortion measures.