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Broadcasters and video service providers are having to adapt their monitoring strategies to a world where much of the physical infrastructure is outside their direct control.

Monitoring the TV content delivery chain has become more complex not just because of convergence between broadband, mobile and broadcast delivery infrastructures, but also proliferation of content sources and heightened expectations over audio visual quality. At the same time new technologies, especially those under the banner of AI and machine learning (ML), are addressing some of the challenges cost effectively. They are even increasing the scope of AV monitoring to achieve higher levels of personalization and adaptation to content as it is played out, as well as adjusting better to the capabilities and display properties of smart TVs or viewing devices themselves.

The Whole Chain Matters

Broadcasters are increasingly aware that monitoring does not begin and end in the delivery chain but has to be applied across the whole content life cycle to be most effective. That is to say, distribution monitoring cannot be divorced form production and especially contribution as it could to a greater extent in the earlier era of exclusive OTA transmission. This has implications not just for the monitoring technology but also for the reporting structures within broadcasters and service providers, requiring collaboration and integration between the relevant departments.

This extension of monitoring across the whole AV lifecycle has happened partly because of increasing implementation of standards such as SMPTE ST 2110, which enables video, audio, and metadata in live production to be separated. This prepares the ground for end-to-end monitoring of broadcast feeds as they progress through different stages, including transcoding and encryption.

The key point is then that issues affecting the ultimate viewing experience can arise and require intervention at any stage, including contribution or ingest. This is where raw feeds are first encoded and transmitted increasingly using ST 2110 standards, but also still SDI in many cases.

While it is true contribution is distinct from the distribution that follows, as it deals directly with the raw, original content, it is decisive in underpinning the quality of the final broadcast, whether that is over the air or streamed, live or linear. Issues around ultimate video quality and audio synchronization arise at this stage and unless addressed they are much harder to rectify later within the distribution chain.

Contribution monitoring is also necessary to meet specifications for resolution, frame rate, and color space, as well as comply with regulatory requirements for audio levels and closed captioning. Here too remedial measures at this stage are less complex and costly to enforce than further along the chain of distribution.

Smart TVs

Yet broadcasters and video service providers are also having to cater for quality control at the other end of the chain from camera to consumer, at the TV itself. This came into the equation with the advent of smart internet connected TVs around two decades ago, becoming predominant over the last five to 10 years. They brought more sophisticated audio and video quality control features, on top of the web connectivity for interaction with end-to-end monitoring systems. 

Recently with the help of machine learning algorithms smart TVs have gained quality control and enhancement features overlapping or competing even more with monitoring systems. That becomes apparent as smart TV makers like Samsung, TCL, LG and Hisense highlight the AI capabilities of the underlying processors for tasks ranging from basic noise reduction to more complex identification and isolation of relevant objects in images. The latter then allows the TV to enhance contrast and color dynamically on the fly for greater visual impact.

Some of these TVs are capable of optimizing HDR tone mapping in movies and shows shot in high dynamic range. They may also be able to effectively re-master older content shot before HDR formats were available, to refine picture detail and sharpness. This is done through algorithms that plunder a database of pre-existing images to converge on the best way of processing the image and simulate HDR.

Such capabilities are leading to smart TVs being subsumed into the end-to-end monitoring infrastructure, with one issue being to ensure there is a single point of authority or control. Many consumers are aware of the conflicts that can occur when signing up for a new subscription TV package delivered through a set top box. Their smart TV may appear to compete with the service for control over the inputs, both broadband and over the air.

Convergence Of Broadcast & Broadband

There is also synergy here with the convergence between broadcast and broadband, being driven for example by the ATSC 3.0 standards in North America and Brazil, and by DVB-I (Interactive) in parts of Europe, as well as some other countries. Although ATSC 3.0 and DVB-I have come at the convergence or coexistence problem from different angles, they both rely ultimately on control of the service as far as the TV set.

ATSC 3.0 is based on the assumption that OTA transmission will continue to predominate for TV viewing with the focus on incorporating IP based delivery over the same infrastructure. It was built for coexistence with 5G over common infrastructure if necessary, sharing some common components, such as IP transport, and support for HTML5/JavaScript. It can deliver audio, video and data to either mobile or fixed terminals over broadcast infrastructure.

DVB-I on the other hand is geared towards broadband and the assumption that broadcasters will migrate their linear services to the internet over time. Or at least it was. The onus initially was on emulating both the quality and style of broadcast linear services over the internet, with support for channel lists. 

But then four and a half years after DVB-I was first published in March 2020, the DVB specification for native IP broadcasting, DVB-NIP (Native IP), was released by ETSI just ahead of IBC 2024.

DVB-NIP enables OTT delivery over DVB broadcast networks, either satellite or terrestrial, so in a sense belatedly moving closer to the ATSC 3.0 approach. The aim was also to reduce cost and complexity of media distribution by converging around a single platform for both IP and broadcast networks. But that platform was now designed to support both broadband and broadcast delivery without any bias or expectation.

This blurs the lines between broadcast and internet distribution and to work effectively will require collaboration among the relevant parties. These are primarily broadcasters, broadband or internet service providers, device makers, and regulators. That will be needed to blend broadcast and broadband more harmoniously and ensure that the DVB’s object of complete transparency for the user being met.

The DVB has also made clear that these standards are aimed at delivering a higher quality, more personalized, and interactive viewing experience, as well as new opportunities for content creators and advertisers. For all this to happen, DVB-I must be able to control the user’s home TV set and support the same channels for potentially simultaneous reception over both DVB-T and the internet.

The idea is that broadcasters can program the system themselves relatively easily to pre-determine which distribution system to prioritize for the same signal, DVB-T or IP. It should be able to switch between the two to obtain the best signal, almost on an “always best connected” principle.

To bring this forward, Mediaset, Italy’s largest commercial broadcaster, has been working with Turkish maker of smart TVs, Vestel, with an emphasis on monitoring right to the TV to optimize viewing quality of experience. The basic idea is that when there is enough bandwidth, the

TV will produce images at 1080p full HD resolution from the internet. But if there are issues such as dropped packets that breach quality thresholds, the TV switches immediately to the broadcast reception signal on the same LCN tuning channel number. Crucially, the user is totally unaware of the switching between broadcast and broadband mode.

Apart from optimizing quality, this may prolong the life of digital terrestrial, which otherwise might be squeezed out as internet bandwidth continues to improve while its capacity remains constrained. DVB-I in this way can combine broadband and broadcast to maximize both quality and resilience against issues or even complete failure on either side.

This is still at a relatively early stage with more work needed on monitoring across the platforms and associated workflows, and here too AI is coming into play. There is scope for improving the level of monitoring by observing common patterns across multiple systems, while filtering out meaningless alerts, which is all very much in the domain of both supervised and unsupervised machine learning. Human operators can focus on higher level tasks, with growing scope for automation of monitoring and even lower-level troubleshooting. 

Automated fixing is particularly critical during live playout, when there is no time for human intervention for troubleshooting. There is though scope even with live streaming services to detect longer term issues and identify emerging instabilities, as they scale to larger audiences for example, for AI and ML to adjust workflows on the basis of network feedback. Then optimum performance can be maintained across the whole service footprint.

To some extent the internet and the various network elements traversed by content, including CDNs as well as fixed and mobile access networks, will continue to look like black boxes to broadcasters and video service providers. But monitoring can tap into various points of the chain, from contribution through to the TV or viewing device, and tune operation at points at which there is control, not just to optimize basic QoS parameters, but also enable those added value personalization features.